Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease

Molecular docking studies and molecular dynamic... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/13-773" }, "headline": "Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive...", "datePublished": "2024-07-08T16:55:49", "dateModified": "2025-05-27T09:37:32", "author": [ { "@type": "Person", "name": "Suggala Ramya Shri" }, { "@type": "Person", "name": "Yogendra Nayak" }, { "@type": "Person", "name": "Sreedhara Ranganath Pai" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background The discovery of an ideal and effective therapy is urgently required for the treatment of Alzheimer’s disease (AD). The main pathological hallmarks of Alzheimer’s disease that appear before the clinical symptoms are neurofibrillary tangles, amyloid plaques, brain inflammation, and neuronal atrophy throughout the cerebral cortex and hippocampus. GSK-3β (Glycogen Synthase Kinase-3β) is regarded as the most important and promising target for therapeutic use because GSK-3β expression levels increase with age and are the most abundant and hyperactive in the brains of patients with AD. GSK-3β activation or upregulation can contribute to neurodegeneration by promoting amyloid beta (Aβ) production and tau hyperphosphorylation. Whereas the underlying mechanism for abnormal production of GSK-3β in AD brains remains unclear. Methods Maestro was used, which is Schrodinger, for our computational simulation studies. In the present work, different modules that were used in previous studies with a little modification, the modules such as Protein Preparation with the help of Protein Preparation Wizard, Ligand Preparation with the help of LigPrep, for ADME (Absorption, Distribution, Metabolism and Excretion) prediction Qikprop was used, for docking studies Glide module was used, Binding energy prediction the Prime was used and Molecular dynamic simulation (MDs) studies done using Desmond. Results Our focus is mainly on an in-silico approach, focusing on library generation; first draw an IMID2 (imidazo [1,5-a]pyridine-3-carboxamide) scaffold structure at Enamine and subjected it to a substructure search to target the receptor grid region (ATP-competitive site) of 6Y9R. They were then subjected to various screening processes. Finally, nine compounds were subjected to MDs studies. Conclusions Nine compounds showed good results with the most stable interactions. Among all the MD studies, the compound (Z3336252116) has shown good interaction and a good docking score. Further experiments and studies are required to confirm these results. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-773/v3", "name": "Molecular docking studies and molecular dynamic simulation analysis:..." } } ] } Home Browse Molecular docking studies and molecular dynamic simulation analysis:... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Shri SR, Nayak Y and Ranganath Pai S. Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.12688/f1000research.145391.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] Suggala Ramya Shri 1 , Yogendra Nayak https://orcid.org/0000-0002-0508-1394 1 , Sreedhara Ranganath Pai https://orcid.org/0000-0002-2017-9533 1 Suggala Ramya Shri 1 , Yogendra Nayak https://orcid.org/0000-0002-0508-1394 1 , Sreedhara Ranganath Pai https://orcid.org/0000-0002-2017-9533 1 PUBLISHED 27 May 2025 Author details Author details 1 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India Suggala Ramya Shri Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Writing – Original Draft Preparation Yogendra Nayak Roles: Formal Analysis, Resources, Supervision, Writing – Review & Editing Sreedhara Ranganath Pai Roles: Conceptualization, Formal Analysis, Methodology, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Bioinformatics gateway. This article is included in the Manipal Academy of Higher Education gateway. Abstract Background The discovery of an ideal and effective therapy is urgently required for the treatment of Alzheimer’s disease (AD). The main pathological hallmarks of Alzheimer’s disease that appear before the clinical symptoms are neurofibrillary tangles, amyloid plaques, brain inflammation, and neuronal atrophy throughout the cerebral cortex and hippocampus. GSK-3β (Glycogen Synthase Kinase-3β) is regarded as the most important and promising target for therapeutic use because GSK-3β expression levels increase with age and are the most abundant and hyperactive in the brains of patients with AD. GSK-3β activation or upregulation can contribute to neurodegeneration by promoting amyloid beta (Aβ) production and tau hyperphosphorylation. Whereas the underlying mechanism for abnormal production of GSK-3β in AD brains remains unclear. Methods Maestro was used, which is Schrodinger, for our computational simulation studies. In the present work, different modules that were used in previous studies with a little modification, the modules such as Protein Preparation with the help of Protein Preparation Wizard, Ligand Preparation with the help of LigPrep, for ADME (Absorption, Distribution, Metabolism and Excretion) prediction Qikprop was used, for docking studies Glide module was used, Binding energy prediction the Prime was used and Molecular dynamic simulation (MDs) studies done using Desmond. Results Our focus is mainly on an in-silico approach, focusing on library generation; first draw an IMID2 (imidazo [1,5-a]pyridine-3-carboxamide) scaffold structure at Enamine and subjected it to a substructure search to target the receptor grid region (ATP-competitive site) of 6Y9R. They were then subjected to various screening processes. Finally, nine compounds were subjected to MDs studies. Conclusions Nine compounds showed good results with the most stable interactions. Among all the MD studies, the compound (Z3336252116) has shown good interaction and a good docking score. Further experiments and studies are required to confirm these results. READ ALL READ LESS Keywords Alzheimer’s disease, GSK-3β, ATP-competitive study, Protein preparation, Ligand Preparation, Qikprop, Glide module, Prime MM-GBSA, Molecular dynamic simulation. Corresponding Author(s) Sreedhara Ranganath Pai ( [email protected] ) Close Corresponding author: Sreedhara Ranganath Pai Competing interests: No competing interests were disclosed. Grant information: Indian Council of Medical Research (ICMR), New Delhi, India, for ICMR-SRF (#2021-11506_F1) to Suggala Ramya Shri. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Shri SR et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Shri SR, Nayak Y and Ranganath Pai S. Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.12688/f1000research.145391.3 ) First published: 08 Jul 2024, 13 :773 ( https://doi.org/10.12688/f1000research.145391.1 ) Latest published: 27 May 2025, 13 :773 ( https://doi.org/10.12688/f1000research.145391.3 ) Revised Amendments from Version 2 In the revised manuscript, the introduction is changed so as to highlight and open up the research question correctly and clearly. A few references are added to make the discussion more meaningful, as per the reviewer's comments. In the revised manuscript, the introduction is changed so as to highlight and open up the research question correctly and clearly. A few references are added to make the discussion more meaningful, as per the reviewer's comments. See the authors' detailed response to the review by Qingchun Zhao See the authors' detailed response to the review by Sachchida Rai and Payal Singh See the authors' detailed response to the review by Shvetank Bhatt See the authors' detailed response to the review by Jigna Samir Shah READ REVIEWER RESPONSES Introduction One of the greatest threats to public health is neurodegenerative diseases because there is no exact therapy. Therefore, the discovery of an ideal and effective therapy is urgently needed for the treatment of AD. 1 Only five to seven cases are due to genetic mutations, and the remaining cases are due to environmental factors and sporadic mutations. The main pathological hallmarks of AD that appear before the clinical symptoms are neurofibrillary tangles, amyloid plaques, brain inflammation, and neuronal atrophy throughout the cerebral cortex and hippocampus. 2 Memory loss can be elicited in Alzheimer’s patients, such as episodic short-term memory impairment followed by a lack of motivation, disorganization, and impairment in solving problems, judgment, and executive functioning. In early-stage impairments, such as visuospatial skills, neuropsychiatric symptoms are most common in mild and late-stage AD. 3 The heritability of AD is estimated to be between 60% and 80%, and these components allow for the identification and determination of pathophysiological processes, diagnostic markers, biological targets, and new treatment targets through genomics translational studies. 4 The aducanumab, a medication based on the Aβ theory, in 2021 FDA approved for the treatment of AD. 5 Lecanemab’s effectiveness and safety in treating early-stage AD require longer study trials. 6 AD is the leading cause of dementia among the elderly, with an estimated 44 million individuals affected globally, a number projected to double by 2050. In the U.S., over 5.5 million are currently diagnosed. The pathophysiology of AD includes several key features: the presence of amyloid plaques, which disrupt neuronal communication and incite inflammatory responses; neurofibrillary tangles formed by hyperphosphorylated tau protein, leading to neuronal dysfunction and cell death; and neuroinflammation, where activated glial cells further damage neurons and exacerbate the disease’s progression. Additionally, AD is characterized by neurotransmitter dysregulation, notably a marked decline in acetylcholine levels affecting cognitive abilities, and imbalances in glutamate neurotransmission, which leads to synaptic dysfunction and potential excitotoxicity. 7 , 8 GSK-3 (Glycogen synthase kinase-3) is a member of the protein kinase family and is widely expressed in tissues. GSK-3 is a Serine/Threonine kinase that transfers a phosphate group to either the serine or threonine residue of its substrate target. 9 GSK-3 is a significant player in regulating structure and metabolic processes in developing neurons as well as adult neurons, but the nervous system can contribute to the disease pathogenesis because overexpression of GSK-3 can contribute to the progressive neurodegenerative conditions like Alzheimer’s. 10 – 12 Overactivation or inhibition of GSK-3 activity in the brain influences sociability skills, mood, emotion, and schizophrenia behavior. 13 – 17 Whereas the reduced activity of GSK-3 can decrease or reverse the severity of a large number of diseases. GSK-3β activation or upregulation can contribute to neurodegeneration by promoting Aβ production and tau hyperphosphorylation whereas the Aβ induces neuronal damage and tau hyperphosphorylation leading to impair the formation of neurofibrillary tangles (NFTs) and their synaptic dysfunction in AD. In general, GSK-3β is highly expressed in the Central Nervous System (CNS) and regulates the hippocampal neurogenesis and biological response of primary immune cells of CNS, microglia, synaptic plasticity, learning, and memory. Activation of GSK-3β kinase has been associated with the inhibition of hippocampal LTP. This hippocampal mechanism is needed for memory formation. 18 GSK-3β is highly expressed in the CNS and its activity is increased in the brain and plasma of AD patients. 19 – 24 Abundant evidence suggests that the inhibition of GSK3β improves both synaptic and cognitive function in AD mouse model studies 25 – 29 and useful therapy for a large of neurological diseases or disorders. The underlying mechanism for abnormal production of GSK-3β in AD remains unclear. 30 Long-term depression (LTD) and long-term potentiation (LTP) are important for regulating the synaptic connections between neurons. 31 The synaptic dysfunction is an early sign of AD. 32 The LTP mainly involves excitotoxicity, plasticity, synaptic formation, learning and memory. The LTP is generally defined as the long-lasting increase in synaptic strength, whereas LTD refers to an opposing process. 33 GSK-3β is expressed in brain regions such as the hippocampus which is involved in learning, and memory and plays an important role in regulating the balance (LTP and LTD). The induction of LTD is associated with a decrease in the phosphorylation of GSK-3β at SER9, whereas the induction of LTP can prevent the LTD occurrence. Finally, inhibiting the active form of GSK-3β is useful for the induction of LTP. 34 Presenilin 1(PS1), a product of the PSEN1 gene, is an important causative factor for familial Alzheimer’s Disease (FAD), and also plays a key role in cleaving APP. 35 GSK-3β-mediated PS1 phosphorylation affects its interaction with N-cadherin and disrupts their binding interaction, finally leading to neuronal activity deficits and synaptic functioning deficits. 36 The interplay among GSK-3β, PS1, and its impact on synaptic functioning deficits and Aβ metabolism can contribute to the pathogenesis of AD. GSK-3β is regarded as the most important and promising therapeutic target. The etiology and pathogenesis of AD are not completely understood. However, available treatments have failed to show novel approaches and effectiveness, and the efficacy of drugs varies from one person to another. 37 GSK-3β expression levels increase with age and are most abundant and hyperactive in the brains of patients with AD. Hence, dysregulation of GSK-3β automatically affects amyloid beta plaques, which have been previously shown in in vitro and in vivo AD models. 7 , 38 GSK-3 plays a very important key role in the metabolic process and regulating structural processes in adult neurons as well as in developing neurons. 39 In the present study, molecular modeling approach was used. For better BBB (Blood Brain Barrier) permeation, the structure was finalized based on a wet lab. So, based on the literature search, a core imidazole scaffold was selected for this study, and from that core imidazole scaffold, the substructures was drawn in the enamine database. Then docking studies were done on the compounds and subjected to ADME, whereas the ADME was predicted with the help of Qikprop. MDs study was conducted on nine compounds. Methods Software used for this computational study Maestro was used for our computational simulation studies; the graphical interface was Schrödinger. In the present work, utilised different modules that were used in previous studies.: Protein Preparation Wizard, Ligand Preparation with the help of LigPrep, for ADME prediction, Qikprop was used, for Docking studies the Glide module was used, and binding energy prediction Prime was used and MDs studies was done by Desmond. 40 - 42 Alternatively freely available software are AutoDock 4, ArgusLab, and Gromacs. Selection and preparation of protein In the present study PDB I.D: 6Y9R ( http://www.rcsb.org/ ) 43 of GSK-3β was selected, which is bound to a co-crystallized ligand with a Resolution of 2.08 Å consisting of only chain A, downloaded from the protein databank site, 41 , 42 and imported this PDB I. D to the Maestro interface. This is followed by preparing the protein molecule with the help of the protein preparation wizard panel of the Schrödinger suite. Alternatively, the AutoDock 4 which is freely available software can be used. The first step for protein preparation is preprocessing in the workspace the protein structure is selected then Assigned bond orders, using the CCD database, explicit hydrogen to the structure, zero-order bonds to metals, creating disulfide bonds, and optimizing missing side chain atoms by running a Prime job, fill in missing loops by running the Prime job was selected and delete waters that are further than specified distance beyond 5.00 Å from any of the het groups including ions. This is recommended for Glide and virtual screening, but MD applications should keep these water as they will help equilibrate the solvent box faster and generate het states using Epik pH: 7.5±0.0, and clicked for preprocessing. The second step is to review and modify here when a new chain, water, or ht is selected to zoom to fit the selection to the workspace, select waters and hets within 5.0 Å of selected chains, and keep the remaining residues and chains as the default setting and generate a state pH of 7.5±0.0. The third step is to refine the selected sample water orientations in addition to other groups, and the protonation states of residues and ligands were set by using PROPKA pH: 7.5 then it simulated the exact experimental conditions and clicked to automatically optimize hydroxyl, ASN, GLN, HIS states using ProtAssign, removing water that is further than beyond hets 3.0 Å, including ions. This is recommended for Glide and virtual screening, but MD applications should keep this water as it will help equilibrate the solvent box faster. OPLS3e force field for the restrained minimization of proteins was done. Then after the minimization, the glycerol and acetate ions were removed from the minimized protein. Ligand selection and preparation IMID 2 scaffolds were used for this study. This scaffold possesses fewer hydrogen bond donors and improved CNS (Central Nervous System) penetration at micromolar concentrations (μM), based on studies by Buonfiglio et al. 44 To generate the focus library, draw an IMID 2 scaffold structure at Enamine and subjected it to a substructure search. Then, enamine realdb_IMID2_Scaffold_molecules (1400 molecules) was downloaded, followed by ligand preparation using LigPrep in the Schrödinger suite software (Schrödinger 2021-3). 45 Alternatively, the AutoDock 4 which is freely available software can be used. The 3D coordinates for the compounds were generated using the LigPrep tool, and the Epik module predicted the most probable charge form for the ionization state of compounds at pH: 7.5±0.0, generated tautomers and stereoisomers, determined chiralities from the 3D structure, generated at most 32 per ligand, and finally, the minimization of the drug molecule process was performed using the force field OPLS3e. 46 , 47 ADME prediction After ligand preparation, all ligands were subjected to QikProp in the Schrödinger suite software to predict the ADME profile of the compounds. QikProp predicts the drug-like properties of all selected compounds, such as molecular weight, Hydrogen Bond Donors, Hydrogen Bond Acceptor, QPlogS (predicted aqueous solubility), QPlogPo/w (predicted octanol/water partition coefficient), QPlogBB (predicted brain/blood partition coefficient), QPlogPw (predicted water/gas partition coefficient), QPlogPC16 (predicted hexadecane/gas partition coefficient), QPlogPoct (predicted octanol/gas partition coefficient), PSA (Van der Waals surface area of polar nitrogen and oxygen atoms), QPPCaco (predicted apparent Caco-2 cell permeability), and QPPMDCK (predicted apparent MDCK cell permeability). 48 Molecular docking studies Here, Schrödinger suite Glide module was used for better prediction of the protein-ligand involved in assessing the fitting of all conformations of compounds at the binding site followed by ranking and modes Schrödinger Release 2021-3. The alternative freely accessible software is AutoDock and Gromacs. The molecular docking study process was used for assessment before molecular docking studies, and the default settings were used for the Receptor Grid generation module. The binding site was recognized using the Receptor Grid generation module, and this binding site was specified as a box of 10 × 10 × 10 Å 3 centered on the centroid of the co-crystallized ligand of GSK-3β, which was taken into consideration for grid generation. 49 Then subject all prepared compounds to the SP (standard precision) scoring function mode. 50 Glide SP docking ligand sampling is in flexible mode and selected sample nitrogen inversions, sample ring conformations, and amides only penalize non-planar conformations, adding Epik state penalties to the docking score. In the output file, the pose viewer file that includes the receptor, written out at most one pose per ligand, performed post-docking minimization, the number of poses per ligand to include was five, and the RMSD was computed to input ligand geometries and run the job. Finally, the results were analyzed, separated, and selected based on the VAL135 residue interaction and physicochemical properties reported in the literature. These compounds were then subjected to the prime-MMGBSA module. MM-GBSA binding energy MM-GBSA (molecular mechanics-generalized born energies & surface area/accessibility continuum solvation method) was used to calculate the binding free energy and binding analysis of 523 SP docked protein-ligand complexes. MM-GBSA exhibits many energy properties for proteins, ligands, and complex structures. Prime MM-GBSA analysis is based on the solvent model of VSGB 2.0, and the OPLS3e force field is used to calculate the binding affinity of the respective protein-ligand complex. 51 MDs with Desmond This model was used to precisely predict the interaction of the ligand with 6Y9R (protein), the stability of their binding under physiological conditions, and to analyze their motion at the molecular and atomic levels. The protein-ligand complex was subjected to a system built here, and different solvent models were selected; an orthorhombic box with dimensions of 10 Å × 10 Å × 10 Å was used to determine the structure geometry to have the minimum box volume, and the checked boundary box is shown in the workspace. The next step is to load the previous system builder file and enter 100ns in simulation time, 1000frames were captured, ensemble class is NPT (Normal Pressure Temperature), 300°K temperature, pressure bar is 1, and relax model system before simulation was used for MDs studies. 52 After MDs, the analysis was done with the help of the RMSD (Root Mean Square Deviation), the RMSD of ligands, RMSD of protein. RMSD is mainly used to determine the stability of the protein-ligand complex, RMSF (Root Mean Square Fluctuations), ligand-protein contacts, and ligand interaction diagram. Alternatively, NAMD molecular simulation along with VMD software can be used which is freely available. Results and Discussion When PDB was searched in UniProt, then 89 GSK-3β human PDBs was identified. Out of 89 PDB entries, 8 PDB literature are yet to be published, and some PDBs with position 3-12 amino acids residual chains C and D are removed from my PDB selection list. PDBs that have chain A/B are taken into consideration, and individually remaining PDBs were studied. Finally, selected this PDB: 6Y9R for this study. 62 The co-crystal structure of 2 was in complex with PDB I. D 6Y9R, which is a GSK-3β enzyme. While INDZ was located at the hinge region, the piperidine chain was exposed to the solvent towards ARG141, Pyridine formed a hydrogen bond with LYS85, nearer to the ligand several hydration sites with residues such as THR138, ASN186, GLN185, LEU132, ASP200, and IIE62 were found to be fully explained in a previous study. The INDZ core scaffold was located in the ATP-competitive binding site of GSK-3β between the N-terminal lobe and the C-terminal lobe region of the protein, and INDZ was previously developed as a novel GSK-3β inhibitor using computational tools. The N-2 of the core and VAL135 N-H group, hydrogen at position N-1 of the core with the ASP133 carbonyl group, hydrogen of the carboxamide group, and VAL135 carbonyl group are important interactions. The R1 substituent appended to the core is oriented towards ARG141 in the external solvent-accessible part of the solvent, and the R2 substituent appended to the LYS85 inner cavity proximity. The indazole N-Ha was then replaced with the C-Ha group, and the ring nitrogen atoms were shuffled, resulting in the IMID2 core. 44 The docked ligands accommodated in the ATP-binding site are similar to the co-crystal structure of the 6Y9R protein. The overlap of the co-crystal structures of the 6Y9R and IMID2 substructures is shown in Figure 1 ( Figures 1A to 1I ). In the previous literature it was reported that a highly flexible piperidine group was exposed to GLN72 and a water bridged H-bond which was involving the R1 oxanyl ring was noticed with PRO136. Figure 1. Overlap of co-crystal structure of 6Y9R and IMID2 substructures. The docking of ligands to a particular receptor grid generated a region of the protein 6Y9R. In this study, SP docking was used. After docking, the compounds were separated and selected based on the VAL135 residue interaction in the literature. Therefore, based on the VAL135 N-H group {the H group of the VAL135 protein should show direct interaction with the nitrogen group of the ligand}. Through this process, 523 molecules of the IMID2 scaffold was finalized. Compounds with good SP scores are listed in Table 1 . Table 1. Intermolecular interactions with amino acids and docking score of the top 9 selected compounds. Name Docking score Structure Bonded interaction Non-bonded interaction Z3336252116 -8.535 H-bond: VAL135, ILE62, ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, ALA83, VAL110, LYS85, CYS199, VAL70 Z3136198649 -8.285 H-bond: VAL135 ILE62, ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, ALA83, VAL110, LYS85, CYS199, VAL70 PV-005996498401 -8.132 H-bond: VAL135 ARG141, THR138, GLU137, PRO136, LEU189, LEU188, TYR134, ASP133, LEU132, VAL110, ALA83, LYS85, CYS199, VAL70, ILE62 PV-0025895352867 -7.541 H-bond: VAL135, PRO136 PI-cation: ARG141 VAL110, ALA83, LYS85, CYS199, VAL70, ILE62, LEU132, ASP133, TYR134, LEU188, GLU137, THR138 Z3603284828 -7.765 H-bond: VAL135 ILE62, ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, ALA83, VAL110, LYS85, CYS199, VAL70 Z3136169169 -8.268 H-bond: VAL135 ARG141, THR138, GLU137, PRO136, LEU189, LEU188, TYR134, ASP133, LEU132, VAL110, ALA83, LYS85, CYS199, VAL70, ILE62 Z2497631540 -7.967 H-bond: VAL135 ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, VAL110, CYS199, ALA83, LYS85, VAL70, ILE62 Z4468779454 -7.306 H-bond: VAL135 ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, VAL110, CYS199, ALA83, LYS85, VAL70, ILE62 Z2708235883 -7.961 H-bond: VAL135 ARG141, THR138, GLU137, PRO136, TYR134, LEU188, ASP133, LEU132, VAL110, CYS199, ALA83, LYS85, VAL70, ILE62 Among all the compounds, the compound (Z3336252116) has shown good docking score (-8.535 Kcal/mol) and with bonding interaction VAL135 which was similar to previous literature (hydrogen of the N-H group of VAL135). ADME prediction The ADME or drug-likeness parameters are the most important and helpful parameters for screening compounds after SP docking studies during the drug discovery process. 53 Generally, for CNS drugs, the physicochemical properties range from molecular weight 100 Da to 450 Da, hydrogen bond acceptors range from 0 to 5, hydrogen bond donor values range from 0 to 3, and the topological polar surface area value is ≤76 Å2. 54 The results of this study are listed in Table 2 . The present study results are having lesser molecular weight and having similar hydrogen bond donor compared to previous ADME properties of 13 trial compounds with an IMID2 core scaffold molecular weight of 393.48, the hydrogen bond donor was 1. In the14 trial compound of the IMID2 core scaffold, the molecular weight was 451.56, and the hydrogen bond donors were 1. In the 15 trial compounds of the IMID2 core scaffold, the molecular weight was 336.39, and the hydrogen bond donors were 1. In 16 trials of the IMID2 core scaffold, the molecular weight was 394.47, and the number of hydrogen bond donors was 1. 44 Table 2. MM-GBSA score and Qikprop ADME prediction results of the top-9 selected compounds. Name MM-GBSA score Molecular weight Hydrogen Bond Donor Hydrogen Bond Acceptor Polar Surface Area Z3336252116 -36.94 252.660 2 3 105.021 Z3136198649 -40.67 232.243 2 4 81.022 PV-005996498401 -33.07 232.243 2 3 101.470 PV-002589535286 -37.94 241.252 2 5.5 78.034 Z3603284828 -40.91 245.282 2 3 72.320 Z3136169169 -40.53 232.243 2 4 81.001 Z2497631540 -42.12 253.688 2 5.7 73.124 Z4468779454 -46.04 247.296 2 5.7 70.083 Z2708235883 -39.16 237.233 2 5.7 73.152 The Z3336252116 with QPlogS value -1.653, QPlogPo/w value was 0.224, QPlogPw value was 14.263, QPlogBB value was -1.042, QPlogPC16 value was 8.612, QPlogPoct values was 15.179, QPPCaco values was 107.434, QPPMDCK values was 226.003. Z3136198649 with QPlogS value -1.249, QPlogPo/w value was 0.180, QPlogPw value was 12.291, QPlogBB value was -0.286, QPlogPC16 value was 8.140, QPlogPoct values was 14.830, QPPCaco values was 155.541, QPPMDCK values was 73.234. PV-005996498401 with QPlogS value -1.416, QPlogPo/w value was 0.223, QPlogPw value was 14.523, QPlogBB values with -1.051, QPlogPC16 value was 8.453, QPlogPoct values was 14.948, QPPCaco values was 159.830, QPPMDCK values was 143.876. PV-0025895352867 with QPlogS value -2.886, QPlogPo/w value was 1.532, QPlogPw value was 11.558, QPlogBB value was -0.639, QPlogPC16 value was 9.016, QPlogPoct values was 14.584, QPPCaco values was 875.662, QPPMDCK values was 428.57. Z3603284828 with QPlogS value -3.257, QPlogPo/w value was 2.117, QPlogPw value was 9.797, QPlogBB value was -0.530, QPlogPC16 value was 8.653, QPlogPoct values was 13.900, QPPCaco values was 1320.626, QPPMDCK values was 668.188. Z3136169169 with QPlogS value -1.258, QPlogPo/w value was 0.181, QPlogPw value was 12.293, QPlogBB value was -0.290, QPlogPC16 value was 8.140, QPlogPoct values was 14.831, QPPCaco values was 155.076, QPPMDCK values was 72.997. Z2497631540 with QPlogS value -3.149, QPlogPo/w value was 1.655, QPlogPw value was 10.508, QPlogBB value was -0.507, QPlogPC16 value was 8.503, QPlogPoct values was 14.075, QPPCaco values was 956.136, QPPMDCK values was 1160.160. Z4468779454 with QPlogS value -2.896, QPlogPo/w value was 1.907, QPlogPw value was 10.321, QPlogBB value was -0.664, QPlogPC16 value was 8.871, QPlogPoct values was 14.100, QPPCaco values was 1311.747, QPPMDCK values was 663.334. Z2708235883 with QPlogS value -2.771, QPlogPo/w value was 1.393, QPlogPw value was 10.536, QPlogBB value was -0.559, QPlogPC16 value was 7.537, QPlogPoct values was 13.626, QPPCaco values was 953.240, QPPMDCK values was 810.911. Among all there parameter the QPlogBB is a useful parameter to predict or reflect directly the ability of a compound to cross the blood-brain barrier partition coefficient. 55 The recommended range of QPlogBB should be within -3.0 to 1.2. 56 If the compound is too polar then the compounds don’t cross the BBB. 57 The QPlogBB negative values indicate that the compounds are poorly permeable and polar. 58 , 59 MDs analysis Compounds were selected based on the screening process described above. Only short-listed compounds were subjected to desmonds for MDs. These studies provide information related to the stability and flexibility of the molecular docking complexes. Performed this study using the MM-GBSA. This study provides information related to protein-ligand interactions, RMSD variation can be assessed, and RMSF fluctuations can be assessed for protein-ligand complexes. The complex was regarded as stable if it fell within the 3 Å range. RMSD generally represents the average difference between the displacements of the atoms at an instant of target structures to the reference structure. As per MD, in comparison to the starting point, how the part of the structure changes or the entire structure over time or to identify large changes in protein structure. RMSF is a calculation of a particular residue or individual residue fluctuation/flexibility or group of atoms during a simulation, related to the reference structure or average structure of simulation. 60 These are the compounds. IMID2 PV-002589535286: During MD simulations, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.6 Å and 1.3 Å over the last 80 ns. The amino acid residue VAL135 formed a 95% direct hydrogen bonding interaction with the amide carbonyl, the amino acid residue VAL135 formed a 42% direct hydrogen bonding interaction with imidazole in the ring structure, the amino acid residue TYR134 had 33% direct hydrogen bonding interaction, and ARG141 had a 72% PI-cation interaction with 1H-imidazole. The amide carbonyl was exposed to an H 2 O molecule through which it interacted with residue ILE62 (49%), as shown in Figure 2 ; however, it was reported in 13 triads (previous literature) that the amide carbonyl was exposed to water-mediated contacts with residues such as ILE62 and GLN185 for the IMID2 core scaffold. The 16-triad isopropoxy group confirmed the probable water molecule interaction with the residue ASP200. The 17 triads showed an indirect interaction between the ortho substituent and residue ASN186. In the 18 triad, the di-F phenyl group was orientated towards LYS85, thus promoting an electrostatic interaction. However, VAL135 interaction formed direct hydrogen bonding interactions with amide carbonyl in all 13, 16, 17, and 18 trial compounds of previous IMID2 scaffolds. 44 Figure 2. Pictorial representation of 6Y9R interactions with the IMID2 compound code PV-002589535286 monitored during MDs trajectory (A) Protein-Ligands RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as histogram; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2 compound code PV-002589535286 was observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues were in greater contact with ligands in the trajectory frame that appeared as a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. IMID2 compound code Z3603284828: During the MDs, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations during the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.3 Å and 0.6 Å over the last 70 ns shown in Figure 3 . The amino acid residue VAL135 formed 93% direct hydrogen bonding interaction with the amide carbonyl, the amino acid residue VAL135 formed 55% direct hydrogen bonding interaction with imidazole, and the amino acid residue ILE62 formed 35% water-mediated direct hydrogen bonding interaction with 1-hydroxycyclopropyl. Figure 3. Pictorial representation of 6Y9R interaction with the IMID2 compound code Z3603284828 monitored during MDs trajectory (A) Protein-Ligands RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as histogram; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2 compound code Z3603284828 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis, a trajectory frame with a number is present, and amino acid residues are seen on the y-axis. The amino acid residues that were in greater contact with ligands in the trajectory frame appeared as a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. IMID2 compound code Z3136169169: During the MDs, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations during the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.8 Å and 0.3 Å over the last 70 ns shown in Figure 4 . The amide carbonyl is exposed to hydrogen bonding and interacts with residue VAL135, and the amino acid residue VAL135 forms 76% direct hydrogen bonding interactions with imidazole. Figure 4. Pictorial representation of 6Y9R interaction with the IMID2 compound code Z3136169169 monitored during MDs trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligands Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as a histogram; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2 compound code Z3136169169 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that were in greater contact with ligands in the trajectory frame appeared as a dark color shade). The VAL135 interaction fraction approached 1.6, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature and protein RMSF is mostly helpful for predicting the changes that occur locally along with the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. IMID2 compound code Z2497631540: During the MDs, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein Cα and ligand structures exhibited higher RMSD fluctuations over the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.4 Å and 3.7 Å over the last 70 ns shown in Figure 5 . The amino acid residue VAL135 formed 84% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 39% direct hydrogen bonding interactions with imidazole in the ring structure. Figure 5. Pictorial representation of 6Y9R interaction with the IMID2 compound code Z2497631540 monitored during MDs trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein contacts; (D) Protein-Ligand Contacts described as a histogram; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2 compound code Z2497631540 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis, a trajectory frame with a number is present, and amino acid residues are seen on the y-axis. The amino acid residues that were in greater contact with ligands in the trajectory frame appeared as a dark color shade). The VAL135 interaction fraction approached 1.2, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. IMID2 compound code Z4468779454: During the MDs, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein Cα and ligand structures exhibited higher RMSD fluctuations over the first 20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.5 Å and 0.4 Å over the last 70 ns shown in Figure 6 . The amino acid residue VAL135 formed a 96% direct hydrogen bonding interaction with the amide carbonyl, the amino acid residue VAL135 formed a 77% direct hydrogen bonding interaction with imidazole, and the amide carbonyl was exposed to the H 2 O molecule through which it interacted with residue ILE62, with 43% and 61% interaction with 1-hydroxypentan-2-yl. Figure 6. Pictorial representation of 6Y9R interaction with the IMID2 compound code Z4468779454 monitored during MDs trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand contacts; (C) Ligand-Protein contacts; (D) Protein-Ligand contacts described as a histogram; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2 compound code Z4468779454 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues were in greater contact with ligands in the trajectory frame that appeared as a dark color shade). The VAL135 interaction fraction approached 1.75, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. IMID2 compound code Z2708235883: During the MDs, the ligand-protein complex exhibited hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations during the first 20 ns. The protein Cα and ligand fluctuations stayed within the range of 0.5 Å and 0.2 Å over the last 70 ns. The amino acid residue VAL135 formed 93% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 53% direct hydrogen bonding interactions with imidazole in the ring structure, as shown in Figure 7 . Figure 7. Pictorial representation of 6Y9R interaction with the IMID2 compound code Z2708235883 monitored during MDs trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand Contacts; (C) Ligand-Protein Contacts; (D) Protein-Ligand Contacts described as a histogram; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2 compound code Z2708235883 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from previous literature, and protein RMSF is mostly helpful for predicting the changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. IMID2_compound code Z3336252116 : During MD simulations, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.6 Å and 2.2 Å over the last 80 ns. The amino acid residue VAL135 formed 93% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 48% direct hydrogen bonding interactions with imidazole in the ring structure, which was exposed to the H 2 O molecule through which it interacted with residue ILE62 (43%), as shown in Figure 8 . Figure 8. Pictorial representation of 6Y9R interaction with the IMID2 compound code Z3336252116 monitored during MDs trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand Contacts; (C) Protein-Ligand Contacts described as a histogram; (D) Ligand-Protein Contacts; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2_compound code Z3336252116 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.5, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein RMSF is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during MDs study. IMID2 compound code Z3136198649 : During MDs, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20 ns. The protein backbone and ligand fluctuations stayed within the range of 0.7 Å and 0.5 Å over the last 80 ns. The amino acid residue VAL135 formed 83% direct hydrogen bonding interaction with amide carbonyl, and the amino acid residue VAL135 formed 49% direct hydrogen bonding interaction with imidazole in the ring structure given in Figure 9 . Figure 9. Pictorial representation of 6Y9R interactions with the IMID2 compound code Z3136198649 monitored during MDs trajectory (A) Protein-Ligands RMSD; (B) Protein-Ligand Contacts; (C) Protein-Ligand Contacts described as a histogram; (D) Ligand-Protein Contacts; (E) Protein RMSF. Interaction diagram of 6Y9R with IMID2 compound code Z3136198649 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.4, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein RMSF is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. IMID2 compound code PV-005996498401 : During MDs, the ligand-protein complex showed hydrogen-bonded interactions. The protein backbone and ligand structure exhibited higher RMSD fluctuations over the first 0-20 ns. The protein backbone and ligand fluctuations stayed within the range of 1.0 Å and 0.6 Å over the last 80 ns. The amino acid residue VAL135 formed 90% direct hydrogen bonding interactions with the amide carbonyl, and the amino acid residue VAL135 formed 47% direct hydrogen bonding interactions with imidazole in the ring structure, as shown in Figure 10 . Figure 10. Pictorial representation of 6Y9R interaction with the IMID2 compound code PV-005996498401 monitored during MDs trajectory (A) Protein-Ligand RMSD; (B) Protein-Ligand contacts; (C) Protein-Ligand contacts described as a histogram; (D) Ligand-Protein contacts; (E) Protein RMSF. Interaction diagram of 6Y9R withIMID2 compound code PV-005996498401 observed during RMSD, Ligand interaction with amino acid residues of Protein Contact during MDs, protein-ligand contacts (cont.) (On the x-axis trajectory frame, the number is present, and amino acid residues are seen on the y-axis. The amino acid residues that have been in more contact with ligands in the trajectory frame appear to have a dark color shade). The VAL135 interaction fraction approached 1.85, whereas the ASP133 interaction fraction was missing in comparison to the core IMID2 scaffold from the previous literature. Protein RMSF is mostly helpful for predicting changes that occur locally along the enzyme chain. These peaks provide information on how much the protein fluctuated during the MDs study. Conclusion In this study, the focused library generation was selected to target the receptor grid region (ATP-competitive site) of 6Y9R. After docking, the compounds were separated and selected based on the VAL135 residue interaction. Further prediction was performed using Qikprop and Prime MM-GBSA assays and MDs studies. Among all the MDs studies the compound (Z3336252116) has shown good interaction and good docking score. Further experimental studies are required to confirm these findings. Ethical considerations Ethics and written consent were not applicable. Data availability Figshare: Molecular docking studies and molecular dynamic simulation to identify GSK-3β inhibitors for AD, https://doi.org/10.6084/m9.figshare.24592716.v1 . 61 The underlying data for this project are: ➢ Enamine_IMID2_Scaffold.csv ➢ Enamine_IMID2_Scaffold_with nitrogen_realdb_molecules 17_35_52.sdf ➢ Ligprep_enamine_IMID2_Scaffold_withnitrogen_realdb_out.csv ➢ Ligprep_enamine_IMID2_SCAFFOLD_WITHNITROGEN_REALDB-out.mae ➢ Prime_mmgbsa_3_out.csv ➢ prime_mmgbsa_3-out.maegz ➢ Prime_mmgbsa_VAL135_INTERACTION_IMID2_ENAMINE_WITHNITROGEN-out.csv ➢ prime_mmgbsa_VAL135_INTERACTION_IMID2_ENAMINE_WITHNITROGEN-out.maegz ➢ QIKPROP_LIGPRE_IMID2_ENAMINE_WITHNITROGEN-out.csv ➢ QIKPROP_LIGPREP_ENAMINE_IMID2_SCAFFOLD_WITHNITROGEN_REALDB-out.mae Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Protein Data Bank: 6Y9R, https://doi.org/10.2210/pdb6Y9R/pdb . 62 Acknowledgments The authors would like to thank the Indian Council of Medical Research (ICMR), New Delhi, India, for the ICMR-SRF (#2021-11506_F1) to Suggala Ramya Shri. References 1. Rippin I, Eldar-Finkelman H: Novel Modality of GSK-3 Inhibition For Treating Neurodegeneration. J Neurol Neuromedicine. 2018; 3 (6): 5–7. Publisher Full Text 2. Sheppard O, Coleman M: Enfermedad de Alzheimer: etiología, neuropatología y patogenia. Enferm Alzheimer Descub fármacos. 2020; 1–22. 3. Morris JC, McDade EM: Alzheimer Disease. Contin Lifelong Learn Neurol. 2022; 28 (3): 648–675. Publisher Full Text 4. Bellenguez C, Küçükali F, Jansen IE, et al. : New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat Genet. 2022; 54 (4): 412–436. PubMed Abstract | Publisher Full Text | Free Full Text 5. Ma Y, Liu S, Zhou Q, et al. : Approved drugs and natural products at clinical stages for treating Alzheimer’s disease.Chin J Nat Med [Internet].2024; 22 (8): 699–710. PubMed Abstract | Publisher Full Text 6. van Dyck CH, Swanson CJ, Aisen P, et al. : Lecanemab in Early Alzheimer’s Disease.N Engl J Med.2023; 388 (1): 9–21. PubMed Abstract | Publisher Full Text 7. Monge-bonilla ASIAC: Molecular Pathogenesis of Alzheimer’s Disease: An Update. Ann Neurosci. 2017; 24 : 46–54. Publisher Full Text 8. Ju Y, Tam KY: Pathological mechanisms and therapeutic strategies for Alzheimer’s disease. Neural Regen Res. 2022; 17 (3): 543–549. Publisher Full Text 9. Pandey MK, DeGrado TR: Glycogen synthase kinase-3 (GSK-3)-targeted therapy and imaging. Vol. 6, Theranostics. Ivyspring International Publisher; 2016 [cited 2021 Apr 14]; pp. 571–593. Free Full Text 10. Spittaels K, Van den Haute C, Van Dorpe J, et al. : Neonatal neuronal overexpression of glycogen synthase kinase-3 beta reduces brain size in transgenic mice. Neuroscience. 2002; 113 : 797–808. PubMed Abstract 11. McCamphill PK, Stoppel LJ, Senter RK, et al. : Glycogen synthase kinase-3 is increased in white cells early in Alzheimer’s disease. Neurosci Lett. 2005; 373 : 1–4. 12. Leroy K, Yilmaz Z, Brion JP: Increased level of active GSK-3beta in Alzheimer’s disease and accumulation in argyrophilic grains and in neurones at different stages of neurofibrillary degeneration. Neuropathol Appl Neurobiol. 2007; 33 : 43–55. PubMed Abstract | Publisher Full Text 13. Latapy C, Rioux V, Guitton MJ, et al. : Selective deletion of forebrain glycogen synthase kinase 3beta reveals a central role in serotonin-sensitive anxiety and social behaviour.Philos Trans R Soc Lond B Biol Sci.2012, 367 : 2460–2474. PubMed Abstract | Publisher Full Text | Free Full Text 14. O’Brien WT, Harper AD, Jove F, et al. : Glycogen synthase kinase-3beta haploinsufficiency mimics the behavioral and molecular effects of lithium.J Neurosci.2004, 24 : 6791–6798. PubMed Abstract | Publisher Full Text | Free Full Text 15. Prickaerts J, Moechars D, Cryns K, et al. : Transgenic mice overexpressing glycogen synthase kinase 3beta: A putative model of hyperactivity and mania. J Neurosci. 2006; 26 : 9022–9029. PubMed Abstract | Publisher Full Text | Free Full Text 16. Kaidanovich-Beilin O, Milman A, Weizman A, et al. : Rapid anti-depressive like activity of specific GSK-3 inhibitor, and its effect on beta-catenin in the mouse hippocampus. Biol Psychiatry. 2004; 55 : 781–784. PubMed Abstract | Publisher Full Text 17. Polter A, Beurel E, Yang S, et al. : Deficiency in the inhibitory serine-phosphorylation of glycogen synthase kinase-3 increases sensitivity to mood disturbances. Neuropsychopharmacology. 2010; 35 : 1761–1774. PubMed Abstract | Publisher Full Text | Free Full Text 18. Lauretti E, Dincer O, Praticò D, et al. : Glycogen synthase kinase-3 signaling in Alzheimer’s disease. Biochim Biophys Acta Mol Cell Res. 2020; 1867 (5): 118664. PubMed Abstract | Publisher Full Text | Free Full Text 19. Llorens-Martin M, Jurado J, Hernandez F, et al. : GSK-3beta, a pivotal kinase in Alzheimer disease. Front Mol Neurosci. 2014; 7 : 46. 20. Platenik J, Fisar Z, Buchal R, et al. : GSK3beta, CREB, and BDNF in peripheral blood of patients with Alzheimer’s disease and depression. Prog Neuropsychopharmacol Biol Psychiatry. 2014; 50 : 83–93. PubMed Abstract | Publisher Full Text 21. Lauretti E, Dincer O, Pratico D: Glycogen synthase kinase-3 signaling in Alzheimer’s disease. Biochim Biophys Acta Mol Cell Res. 2020; 1867 (5): 118664. PubMed Abstract | Publisher Full Text | Free Full Text 22. Jope RS, Cheng Y, Lowell JA, et al. : Stressed and inflamed, can GSK3 be blamed? Trends Biochem Sci. 2017; 42 (3): 180–192. PubMed Abstract | Publisher Full Text | Free Full Text 23. Fuster-Matanzo A, Jurado-Arjona J, Benvegnu S, et al. : Glycogen synthase kinase-3beta regulates fractalkine production by altering its trafficking from Golgi to plasma membrane: implications for Alzheimer’s disease. Cell Mol Life Sci. 2017; 74 (6): 1153–1163. PubMed Abstract | Publisher Full Text | Free Full Text 24. Maixner DW, Weng HR: The role of glycogen synthase kinase 3 beta in neuroinflammation and pain. J Pharm Pharmacol. 2013; 1 (1): 001. 25. Farr SA, Sandoval KE, Niehoff ML, et al. : Peripheral administration of GSK-3beta antisense oligonucleotide improves learning and memory in SAMP8 and Tg2576 mouse models of Alzheimer’s disease. J Alzheimers Dis. 2016; 54 (4): 1339–1348. PubMed Abstract | Publisher Full Text 26. Franklin AV, King MK, Palomo V, et al. : Glycogen synthase kinase-3 inhibitors reverse deficits in longterm potentiation and cognition in fragile X mice. Biol Psychiatry. 2014; 75 (3): 198–206. PubMed Abstract | Publisher Full Text | Free Full Text 27. Farr SA, Ripley JL, Sultana R, et al. : Antisense oligonucleotide against GSK-3beta in brain of SAMP8 mice improves learning and memory and decreases oxidative stress: Involvement of transcription factor Nrf2 and implications for Alzheimer disease. Free Radic Biol Med. 2014; 67 : 387–395. PubMed Abstract | Publisher Full Text | Free Full Text 28. King MK, Pardo M, Cheng Y, et al. : Glycogen synthase kinase-3 inhibitors: Rescuers of cognitive impairments. Pharmacol Ther. 2014; 141 (1): 1–12. PubMed Abstract | Publisher Full Text | Free Full Text 29. Hernandez F, Borrell J, Guaza C, et al. : Spatial learning deficit in transgenic mice that conditionally over-express GSK-3beta in the brain but do not form tau filaments. J Neurochem. 2002; 83 (6): 1529–1533. PubMed Abstract | Publisher Full Text 30. Human tau accumulation promotes glycogen synthase kinase-3b acetylation and thus upregulates the kinase: A vicious cycle in Alzheimer neurodegeneration (PIIS2352396422001542). 31. Warpechowski M, Warpechowski J, Kulczyńska-Przybik A, et al. : Biomarkers of activity-dependent plasticity and persistent enhancement of synaptic transmission in Alzheimer disease: a review of the current status. Med Sci Monit. 2023; 29 : e938826. PubMed Abstract | Publisher Full Text | Free Full Text 32. Tönnies E, Trushina E: Oxidative stress, synaptic dysfunction, and Alzheimer’s disease. J Alzheimers Dis. 2017; 57 : 1105–1121. PubMed Abstract | Publisher Full Text | Free Full Text 33. Vyklicky V, Korinek M, Smejkalova T, et al. : Structure, function, and pharmacology of NMDA receptor channels. Physiol Res. 2014; 63 : S191–S203. Publisher Full Text 34. Peineau S, Taghibiglou C, Bradley C, et al. : LTP inhibits LTD in the hippocampus via regulation of GSK3beta. Neuron. 2007; 53 : 703–717. PubMed Abstract | Publisher Full Text 35. Giau VV, Bagyinszky E, Youn YC, et al. : APP, PSEN1, and PSEN2 mutations in Asian patients with early-onset Alzheimer disease. Int J Mol Sci. 2019; 20 : 4757. PubMed Abstract | Publisher Full Text | Free Full Text 36. Uemura K, Kuzuya A, Shimozono Y, et al. : GSK3beta activity modifies the localization and function of presenilin 1. J Biol Chem. 2007; 282 : 15823–15832. PubMed Abstract | Publisher Full Text 37. Shri SR, Manandhar S, Nayak Y, et al. : Role of GSK-3β Inhibitors: New Promises and Opportunities for Alzheimer’s Disease. Adv Pharm Bull. 2023; 13 : 688–700. PubMed Abstract | Publisher Full Text | Free Full Text 38. Lauretti E, Dincer O, Praticò D: Glycogen synthase kinase-3 signaling in Alzheimer’s disease. Biochim Biophys Acta Mol Cell Res. 2020; 1867 (5): 118664. PubMed Abstract | Publisher Full Text | Free Full Text 39. Rippin I, Eldar-Finkelman H: Mechanisms and therapeutic implications of gsk-3 in treating neurodegeneration. Cells. 2021; 10 (2): 1–22. Publisher Full Text 40. Baby K, Maity S, Mehta CH, et al. : Computational drug repurposing of Akt - 1 allosteric inhibitors for non-small cell lung cancer. Sci Rep. 2023; 13 : 1–25. PubMed Abstract | Publisher Full Text | Free Full Text 41. Baby K, Maity S, Mehta CH, et al. : Targeting SARS-CoV-2 RNA-dependent RNA polymerase: An in silico drug repurposing for COVID-19. F1000Res. 2020; 9 (September): 1166. PubMed Abstract | Publisher Full Text | Free Full Text 42. Priya K, Manandhar S, Sankhe R, et al. : Structure Based Virtual Docking and Molecular Dynamics Guided Identification of Potential Phytoconstituents from Traditionally Used Female Antifertility Plant. Tabriz Univ Med Sci. 2022; 28 (2): 285–294. Publisher Full Text 43. Altunkaya A, Bi C, Bradley AR, et al. : OUP accepted manuscript. Nucleic Acids Res. 2016; 45 (October 2016): 271–281. 44. Buonfiglio R, Prati F, Bischetti M, et al. : Discovery of novel imidazopyridine GSK-3β inhibitors supported by computational approaches. Molecules. 2020; 25 (9). PubMed Abstract | Publisher Full Text | Free Full Text 45. Madhavi Sastry G, Adzhigirey M, Day T, et al. : Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des. 2013; 27 (3): 221–234. PubMed Abstract | Publisher Full Text 46. Roos K, Wu C, Damm W, et al. : OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules. J Chem Theory Comput. 2019; 15 (3): 1863–1874. PubMed Abstract | Publisher Full Text 47. Chen IJ, Foloppe N: Drug-like bioactive structures and conformational coverage with the ligprep/confgen suite: Comparison to programs MOE and catalyst. J Chem Inf Model. 2010; 50 (5): 822–839. PubMed Abstract | Publisher Full Text 48. Manual U: Schrödinger. Schrödinger.1987. 49. Kumar A, Rathi E, Kini SG: E-pharmacophore modelling, virtual screening, molecular dynamics simulations and in-silico ADME analysis for identification of potential E6 inhibitors against cervical cancer. J Mol Struct. 2019; 1189 : 299–306. Publisher Full Text 50. Kumar A, Rathi E, Kini SG: Drug repurposing approach for the identification and designing of potential E6 inhibitors against cervical cancer: an in silico investigation. Struct Chem. 2020; 31 (1): 141–153. Publisher Full Text 51. Li J, Abel R, Zhu K, et al. : The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. Proteins Struct Funct Bioinforma. 2011; 79 (10): 2794–2812. PubMed Abstract | Publisher Full Text | Free Full Text 52. Bowers KJ, Chow E, Xu H, et al. : Scalable algorithms for molecular dynamics simulations on commodity clusters. Proc 2006 ACM/IEEE Conf Supercomput SC’06. 2006;(November). 53. Attique SA, Hassan M, Usman M, et al. : A molecular docking approach to evaluate the pharmacological properties of natural and synthetic treatment candidates for use against hypertension. Int J Environ Res Public Health. 2019; 16 (6): 1–17. 54. Kharkar PS: Drugs acting on central nervous system (CNS) targets as leads for non-CNS targets. F1000Res. 2014; 3 : 1–7. Publisher Full Text 55. Liu Y, Tu Y, Kang Y, et al. : Biological evaluation, molecular modeling and dynamics simulation of phenanthrenes isolated from Bletilla striata as butyrylcholinesterase inhibitors. Sci Rep. 2022 Aug 11; 12 (1): 13649. PubMed Abstract | Publisher Full Text | Free Full Text 56. Tang C, Ye Y, Feng Y, et al. : TCM, brain function and drug space. Nat Prod Rep. 2016; 33 (1): 6–25. PubMed Abstract | Publisher Full Text 57. James JP, Ail PD, Crasta L, et al. : In silico ADMET and molecular interaction profiles of phytochemicals from medicinal plants in Dakshina Kannada. Journal of Health and Allied Sciences NU. 2024 Apr; 14 (02): 190–201. Publisher Full Text 58. Divyashri G, Murthy TK, Sundareshan S, et al. : In silico approach towards the identification of potential inhibitors from Curcuma amada Roxb against H. pylori: ADMET screening and molecular docking studies BioImpacts. 2021; 11 (2): 119–127. Publisher Full Text 59. Rippin I, Eldar-Finkelman H: Mechanisms and therapeutic implications of GSK-3 in treating neurodegeneration. Cells. 2021 Jan 29; 10 (2): 262. PubMed Abstract | Publisher Full Text | Free Full Text 60. Martínez L: Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis. PLoS One. 2015 Mar 27; 10 (3): e0119264. PubMed Abstract | Publisher Full Text | Free Full Text 61. Shri SR, Nayak Y, Pai KSR: Molecular docking studies and molecular dynamic simulation to identify GSK-3β inhibitors for Alzheimer’s disease. Dataset. figshare. 2023. Publisher Full Text 62. Krapp S, Griessner A, Blaesse M, et al. : Crystal structure of GSK-3b in complex with the 1H-indazole-3-carboxamide inhibitor 2. Protein Data Bank. 2020. Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 08 Jul 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India Suggala Ramya Shri Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Writing – Original Draft Preparation Yogendra Nayak Roles: Formal Analysis, Resources, Supervision, Writing – Review & Editing Sreedhara Ranganath Pai Roles: Conceptualization, Formal Analysis, Methodology, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information Indian Council of Medical Research (ICMR), New Delhi, India, for ICMR-SRF (#2021-11506_F1) to Suggala Ramya Shri. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) version 3 Revised Published: 27 May 2025, 13:773 https://doi.org/10.12688/f1000research.145391.3 version 2 Revised Published: 27 Aug 2024, 13:773 https://doi.org/10.12688/f1000research.145391.2 version 1 Published: 08 Jul 2024, 13:773 https://doi.org/10.12688/f1000research.145391.1 Copyright © 2025 Shri SR et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Shri SR, Nayak Y and Ranganath Pai S. Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.12688/f1000research.145391.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 27 Aug 2024 Revised Views 0 Cite How to cite this report: Rai S and Singh P. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.170347.r318530 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v2#referee-response-318530 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 28 Sep 2024 Sachchida Rai , Centre of Experimental Medicine and Surgery (CEMS), Institute of Medical Sciences (IMS), Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India Payal Singh , Department of Zoology, Banaras Hindu University, Varanasi, Uttar Pradesh, India Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.170347.r318530 I have some questions on the manuscript which are as follows. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? What specific modifications were made to the previously used computational ... Continue reading READ ALL I have some questions on the manuscript which are as follows. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy? Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered? How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies? Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study? Have you considered testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models? Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time? How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline? Was there any off-target effects observed for the identified inhibitors in the computational analyses? Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), & Rai SN, et al., 2020 (Ref 6) in your manuscript. Could targeting GSK-3β alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach? How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R? What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity? How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation? Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes? Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency? How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations? What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process? How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity? Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding? How do you plan to prioritize the compounds for further experimental testing, given the computational predictions? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Ramakrishna K, Karuturi P, Siakabinga Q, T A G, et al.: Indole-3 Carbinol and Diindolylmethane Mitigated β-Amyloid-Induced Neurotoxicity and Acetylcholinesterase Enzyme Activity: In Silico, In Vitro, and Network Pharmacology Study. Diseases . 2024; 12 (8). PubMed Abstract | Publisher Full Text 2. Tripathi PN, Lodhi A, Rai SN, Nandi NK, et al.: Review of Pharmacotherapeutic Targets in Alzheimer's Disease and Its Management Using Traditional Medicinal Plants. Degener Neurol Neuromuscul Dis . 2024; 14 : 47-74 PubMed Abstract | Publisher Full Text 3. Singh M, Agarwal V, Pancham P, Jindal D, et al.: A Comprehensive Review and Androgen Deprivation Therapy and Its Impact on Alzheimer's Disease Risk in Older Men with Prostate Cancer. Degener Neurol Neuromuscul Dis . 2024; 14 : 33-46 PubMed Abstract | Publisher Full Text 4. Tripathi PN, Srivastava P, Sharma P, Tripathi MK, et al.: Biphenyl-3-oxo-1,2,4-triazine linked piperazine derivatives as potential cholinesterase inhibitors with anti-oxidant property to improve the learning and memory. Bioorg Chem . 2019; 85 : 82-96 PubMed Abstract | Publisher Full Text 5. Srivastava P, Tripathi PN, Sharma P, Rai SN, et al.: Design and development of some phenyl benzoxazole derivatives as a potent acetylcholinesterase inhibitor with antioxidant property to enhance learning and memory. Eur J Med Chem . 2019; 163 : 116-135 PubMed Abstract | Publisher Full Text 6. Rai SN, Singh C, Singh A, Singh MP, et al.: Mitochondrial Dysfunction: a Potential Therapeutic Target to Treat Alzheimer's Disease. Mol Neurobiol . 2020; 57 (7): 3075-3088 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: My research focuses on exploring the molecular mechanisms and therapeutic interventions for neurodegenerative diseases, particularly Alzheimer's and Parkinson's disease. I investigate the roles of biomarkers, signaling pathways, and epigenetic regulators in disease progression. My work integrates computational biology, drug discovery, and experimental models to identify novel therapeutic targets, including GSK-3β and other key enzymes. I also explore natural compounds, such as those derived from nutraceuticals and endophytic fungi, for their neuroprotective potential. Additionally, I study autophagy, oxidative stress, and inflammation as critical factors in neurodegeneration, aiming to develop targeted strategies for chronic disease mitigation and brain health. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Rai S and Singh P. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.170347.r318530 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v2#referee-response-318530 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 16 Jun 2025 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 16 Jun 2025 Author Response Comment 1. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? Answer to the comment: The binding affinity and specificity are generally ... Continue reading Comment 1. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? Answer to the comment: The binding affinity and specificity are generally related in that high binding affinity usually leads to high specificity for a particular target. The binding affinity and specificity of GSK-3β inhibitors vary depending on the type of inhibitors/ lignads used. Comment 2. What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy? Answer to the comment: Instead of a similarity structure in this study, substructure search was used for IMID2 scaffold at Enamine and the compounds were subjected to Qikprop to predict the accuracy of the compounds to cross the BBB (QPlogBB). In a previous study, QPlogBB was not used, whereas other physicochemical parameters were used, such as molecular weight, hydrogen bond donor and hydrogen bond acceptor. Comment 3. Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered? Answer to the comment: In this study, the substructure compound search was used for ligand identification. All the compounds used in this study were novel, but scaffold was similar to previous literature. Comment 4. How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies? Answer to the comment: Absorption is how the drugs go through the organs of the body to reach the systemic circulation. Distribution of the drug/compound transported from one tissue to another tissue or from one organ to another organ. The transportation or distribution of compounds/drugs into the brain/CNS is the main focus in drug discovery. The metabolism is also referred to as the biotransformation of exogenous compounds/drugs to increase their water solubility and hydrophilicity. Finally, the water solubility facilitates their excretion process. Using the information to evaluate the compound's drug safety and risk outcomes. Comment 5. Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study? Answer to the comment: As the computation methods are regularly and routinely used, there are no challenges encountered in using the computational simulation tools. Comment 6. Have you considered testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models? Answer to the comment: No, yet to consider testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models. Comment 7. Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time? Answer to the comment: Yes, the molecular dynamics (MD) simulations can provide insights into the stability and conformational changes of protein-ligand complexes over time. MD simulation studies predict how protein-ligand interactions occur, analyse dynamic changes and conformational changes in the proteins. Comment 8. How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline? Answer to the comment: The finalized compounds will be subjected to in vitro and in vivo studies. If the molecules are showing good results, they can be translated into clinical studies. Comment 9. Was there any off-target effects observed for the identified inhibitors in the computational analyses? Answer to the comment: No such off-target effects were observed for inhibitors in computational analyses using the Schrodinger Maestro tool. Comment 10. Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), & Rai SN, et al., 2020 (Ref 6) in your manuscript. Answer to the comment: These references are not related to our objectives, hence these are not included as relevant references. Comment 11. Could targeting GSK-3β alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach? Answer to the comment: As most of the current study focuses on targeting GSK-3β, and the preclinical data to a greater extent prevent Alzheimer’s disease, however, the question of whether GSK-3β alone is sufficient to address the complex pathology of Alzheimer's disease remains to be answered through clinical investigations. Comment 12. How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R? Answer to the comment: The identified GSK-3β inhibitors particularly bind to the specific ATP-competitive site in 6Y9R protein region and reduce the off-target effects. Comment 13. What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity? Answer to the comment: This was based on previous literature, in this study N-region of the core and VAL135 at N-H group of the ligand. Comment 14. How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation? Answer to the comment: Qikprop: Predicts pharmaceutically relevant properties of the compounds and physically significant descriptors of an individual compound. Prime MM-GBSA: Predicts the protein-ligand binding free energy of an individual compound. This was accurate to estimate the relative binding free energy of the compound/ligand. Comment 15. Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes? Answer to the comment: We have elaborated this part in the revised manuscript. Comment 16. Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency? Answer to the comment: The alternative residue, PRO 136, forms a hydrogen bond interaction with the core of IMID2. Comment 17. How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations? Answer to the comment: The docking score gives an idea about the score of a compound and is used to predict the binding affinity of protein and ligand when it is subjected to a molecular docking study. Qikprop gives an idea about the compound's ability to cross the BBB or not. The MM-GBSA gives an idea about the sum of intermolecular interactions present between the protein and ligand. Comment 18. What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process? Answer to the comment: In the present study, a molecular docking study was used to subject these compounds to Qikprop, then the compounds which cross the standard range of physicochemical properties. Those compounds were segregated based on VAL135 at N-H group followed by subjecting those compounds to Prime MM-GBSA and Molecular dynamic simulation studies. Comment 19. How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity? Answer to the comment: The identified compounds are better compared to the existing inhibitors of 6Y9R. Comment 20. Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding? Answer to the comment: The interactions during the MD simulation were stable for a larger period, hence, the shortlisted molecules can be better GSK-3β inhibitors. Comment 21. How do you plan to prioritize the compounds for further experimental testing, given the computational predictions? Answer to the comment: Yes, the compounds will be further subjected to in vitro studies and later to in vivo studies. Comment 1. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? Answer to the comment: The binding affinity and specificity are generally related in that high binding affinity usually leads to high specificity for a particular target. The binding affinity and specificity of GSK-3β inhibitors vary depending on the type of inhibitors/ lignads used. Comment 2. What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy? Answer to the comment: Instead of a similarity structure in this study, substructure search was used for IMID2 scaffold at Enamine and the compounds were subjected to Qikprop to predict the accuracy of the compounds to cross the BBB (QPlogBB). In a previous study, QPlogBB was not used, whereas other physicochemical parameters were used, such as molecular weight, hydrogen bond donor and hydrogen bond acceptor. Comment 3. Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered? Answer to the comment: In this study, the substructure compound search was used for ligand identification. All the compounds used in this study were novel, but scaffold was similar to previous literature. Comment 4. How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies? Answer to the comment: Absorption is how the drugs go through the organs of the body to reach the systemic circulation. Distribution of the drug/compound transported from one tissue to another tissue or from one organ to another organ. The transportation or distribution of compounds/drugs into the brain/CNS is the main focus in drug discovery. The metabolism is also referred to as the biotransformation of exogenous compounds/drugs to increase their water solubility and hydrophilicity. Finally, the water solubility facilitates their excretion process. Using the information to evaluate the compound's drug safety and risk outcomes. Comment 5. Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study? Answer to the comment: As the computation methods are regularly and routinely used, there are no challenges encountered in using the computational simulation tools. Comment 6. Have you considered testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models? Answer to the comment: No, yet to consider testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models. Comment 7. Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time? Answer to the comment: Yes, the molecular dynamics (MD) simulations can provide insights into the stability and conformational changes of protein-ligand complexes over time. MD simulation studies predict how protein-ligand interactions occur, analyse dynamic changes and conformational changes in the proteins. Comment 8. How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline? Answer to the comment: The finalized compounds will be subjected to in vitro and in vivo studies. If the molecules are showing good results, they can be translated into clinical studies. Comment 9. Was there any off-target effects observed for the identified inhibitors in the computational analyses? Answer to the comment: No such off-target effects were observed for inhibitors in computational analyses using the Schrodinger Maestro tool. Comment 10. Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), & Rai SN, et al., 2020 (Ref 6) in your manuscript. Answer to the comment: These references are not related to our objectives, hence these are not included as relevant references. Comment 11. Could targeting GSK-3β alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach? Answer to the comment: As most of the current study focuses on targeting GSK-3β, and the preclinical data to a greater extent prevent Alzheimer’s disease, however, the question of whether GSK-3β alone is sufficient to address the complex pathology of Alzheimer's disease remains to be answered through clinical investigations. Comment 12. How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R? Answer to the comment: The identified GSK-3β inhibitors particularly bind to the specific ATP-competitive site in 6Y9R protein region and reduce the off-target effects. Comment 13. What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity? Answer to the comment: This was based on previous literature, in this study N-region of the core and VAL135 at N-H group of the ligand. Comment 14. How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation? Answer to the comment: Qikprop: Predicts pharmaceutically relevant properties of the compounds and physically significant descriptors of an individual compound. Prime MM-GBSA: Predicts the protein-ligand binding free energy of an individual compound. This was accurate to estimate the relative binding free energy of the compound/ligand. Comment 15. Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes? Answer to the comment: We have elaborated this part in the revised manuscript. Comment 16. Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency? Answer to the comment: The alternative residue, PRO 136, forms a hydrogen bond interaction with the core of IMID2. Comment 17. How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations? Answer to the comment: The docking score gives an idea about the score of a compound and is used to predict the binding affinity of protein and ligand when it is subjected to a molecular docking study. Qikprop gives an idea about the compound's ability to cross the BBB or not. The MM-GBSA gives an idea about the sum of intermolecular interactions present between the protein and ligand. Comment 18. What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process? Answer to the comment: In the present study, a molecular docking study was used to subject these compounds to Qikprop, then the compounds which cross the standard range of physicochemical properties. Those compounds were segregated based on VAL135 at N-H group followed by subjecting those compounds to Prime MM-GBSA and Molecular dynamic simulation studies. Comment 19. How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity? Answer to the comment: The identified compounds are better compared to the existing inhibitors of 6Y9R. Comment 20. Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding? Answer to the comment: The interactions during the MD simulation were stable for a larger period, hence, the shortlisted molecules can be better GSK-3β inhibitors. Comment 21. How do you plan to prioritize the compounds for further experimental testing, given the computational predictions? Answer to the comment: Yes, the compounds will be further subjected to in vitro studies and later to in vivo studies. Competing Interests: Authors declare that there is no competing interest. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 16 Jun 2025 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 16 Jun 2025 Author Response Comment 1. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? Answer to the comment: The binding affinity and specificity are generally ... Continue reading Comment 1. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? Answer to the comment: The binding affinity and specificity are generally related in that high binding affinity usually leads to high specificity for a particular target. The binding affinity and specificity of GSK-3β inhibitors vary depending on the type of inhibitors/ lignads used. Comment 2. What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy? Answer to the comment: Instead of a similarity structure in this study, substructure search was used for IMID2 scaffold at Enamine and the compounds were subjected to Qikprop to predict the accuracy of the compounds to cross the BBB (QPlogBB). In a previous study, QPlogBB was not used, whereas other physicochemical parameters were used, such as molecular weight, hydrogen bond donor and hydrogen bond acceptor. Comment 3. Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered? Answer to the comment: In this study, the substructure compound search was used for ligand identification. All the compounds used in this study were novel, but scaffold was similar to previous literature. Comment 4. How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies? Answer to the comment: Absorption is how the drugs go through the organs of the body to reach the systemic circulation. Distribution of the drug/compound transported from one tissue to another tissue or from one organ to another organ. The transportation or distribution of compounds/drugs into the brain/CNS is the main focus in drug discovery. The metabolism is also referred to as the biotransformation of exogenous compounds/drugs to increase their water solubility and hydrophilicity. Finally, the water solubility facilitates their excretion process. Using the information to evaluate the compound's drug safety and risk outcomes. Comment 5. Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study? Answer to the comment: As the computation methods are regularly and routinely used, there are no challenges encountered in using the computational simulation tools. Comment 6. Have you considered testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models? Answer to the comment: No, yet to consider testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models. Comment 7. Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time? Answer to the comment: Yes, the molecular dynamics (MD) simulations can provide insights into the stability and conformational changes of protein-ligand complexes over time. MD simulation studies predict how protein-ligand interactions occur, analyse dynamic changes and conformational changes in the proteins. Comment 8. How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline? Answer to the comment: The finalized compounds will be subjected to in vitro and in vivo studies. If the molecules are showing good results, they can be translated into clinical studies. Comment 9. Was there any off-target effects observed for the identified inhibitors in the computational analyses? Answer to the comment: No such off-target effects were observed for inhibitors in computational analyses using the Schrodinger Maestro tool. Comment 10. Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), & Rai SN, et al., 2020 (Ref 6) in your manuscript. Answer to the comment: These references are not related to our objectives, hence these are not included as relevant references. Comment 11. Could targeting GSK-3β alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach? Answer to the comment: As most of the current study focuses on targeting GSK-3β, and the preclinical data to a greater extent prevent Alzheimer’s disease, however, the question of whether GSK-3β alone is sufficient to address the complex pathology of Alzheimer's disease remains to be answered through clinical investigations. Comment 12. How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R? Answer to the comment: The identified GSK-3β inhibitors particularly bind to the specific ATP-competitive site in 6Y9R protein region and reduce the off-target effects. Comment 13. What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity? Answer to the comment: This was based on previous literature, in this study N-region of the core and VAL135 at N-H group of the ligand. Comment 14. How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation? Answer to the comment: Qikprop: Predicts pharmaceutically relevant properties of the compounds and physically significant descriptors of an individual compound. Prime MM-GBSA: Predicts the protein-ligand binding free energy of an individual compound. This was accurate to estimate the relative binding free energy of the compound/ligand. Comment 15. Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes? Answer to the comment: We have elaborated this part in the revised manuscript. Comment 16. Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency? Answer to the comment: The alternative residue, PRO 136, forms a hydrogen bond interaction with the core of IMID2. Comment 17. How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations? Answer to the comment: The docking score gives an idea about the score of a compound and is used to predict the binding affinity of protein and ligand when it is subjected to a molecular docking study. Qikprop gives an idea about the compound's ability to cross the BBB or not. The MM-GBSA gives an idea about the sum of intermolecular interactions present between the protein and ligand. Comment 18. What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process? Answer to the comment: In the present study, a molecular docking study was used to subject these compounds to Qikprop, then the compounds which cross the standard range of physicochemical properties. Those compounds were segregated based on VAL135 at N-H group followed by subjecting those compounds to Prime MM-GBSA and Molecular dynamic simulation studies. Comment 19. How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity? Answer to the comment: The identified compounds are better compared to the existing inhibitors of 6Y9R. Comment 20. Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding? Answer to the comment: The interactions during the MD simulation were stable for a larger period, hence, the shortlisted molecules can be better GSK-3β inhibitors. Comment 21. How do you plan to prioritize the compounds for further experimental testing, given the computational predictions? Answer to the comment: Yes, the compounds will be further subjected to in vitro studies and later to in vivo studies. Comment 1. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? Answer to the comment: The binding affinity and specificity are generally related in that high binding affinity usually leads to high specificity for a particular target. The binding affinity and specificity of GSK-3β inhibitors vary depending on the type of inhibitors/ lignads used. Comment 2. What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy? Answer to the comment: Instead of a similarity structure in this study, substructure search was used for IMID2 scaffold at Enamine and the compounds were subjected to Qikprop to predict the accuracy of the compounds to cross the BBB (QPlogBB). In a previous study, QPlogBB was not used, whereas other physicochemical parameters were used, such as molecular weight, hydrogen bond donor and hydrogen bond acceptor. Comment 3. Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered? Answer to the comment: In this study, the substructure compound search was used for ligand identification. All the compounds used in this study were novel, but scaffold was similar to previous literature. Comment 4. How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies? Answer to the comment: Absorption is how the drugs go through the organs of the body to reach the systemic circulation. Distribution of the drug/compound transported from one tissue to another tissue or from one organ to another organ. The transportation or distribution of compounds/drugs into the brain/CNS is the main focus in drug discovery. The metabolism is also referred to as the biotransformation of exogenous compounds/drugs to increase their water solubility and hydrophilicity. Finally, the water solubility facilitates their excretion process. Using the information to evaluate the compound's drug safety and risk outcomes. Comment 5. Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study? Answer to the comment: As the computation methods are regularly and routinely used, there are no challenges encountered in using the computational simulation tools. Comment 6. Have you considered testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models? Answer to the comment: No, yet to consider testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models. Comment 7. Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time? Answer to the comment: Yes, the molecular dynamics (MD) simulations can provide insights into the stability and conformational changes of protein-ligand complexes over time. MD simulation studies predict how protein-ligand interactions occur, analyse dynamic changes and conformational changes in the proteins. Comment 8. How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline? Answer to the comment: The finalized compounds will be subjected to in vitro and in vivo studies. If the molecules are showing good results, they can be translated into clinical studies. Comment 9. Was there any off-target effects observed for the identified inhibitors in the computational analyses? Answer to the comment: No such off-target effects were observed for inhibitors in computational analyses using the Schrodinger Maestro tool. Comment 10. Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), & Rai SN, et al., 2020 (Ref 6) in your manuscript. Answer to the comment: These references are not related to our objectives, hence these are not included as relevant references. Comment 11. Could targeting GSK-3β alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach? Answer to the comment: As most of the current study focuses on targeting GSK-3β, and the preclinical data to a greater extent prevent Alzheimer’s disease, however, the question of whether GSK-3β alone is sufficient to address the complex pathology of Alzheimer's disease remains to be answered through clinical investigations. Comment 12. How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R? Answer to the comment: The identified GSK-3β inhibitors particularly bind to the specific ATP-competitive site in 6Y9R protein region and reduce the off-target effects. Comment 13. What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity? Answer to the comment: This was based on previous literature, in this study N-region of the core and VAL135 at N-H group of the ligand. Comment 14. How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation? Answer to the comment: Qikprop: Predicts pharmaceutically relevant properties of the compounds and physically significant descriptors of an individual compound. Prime MM-GBSA: Predicts the protein-ligand binding free energy of an individual compound. This was accurate to estimate the relative binding free energy of the compound/ligand. Comment 15. Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes? Answer to the comment: We have elaborated this part in the revised manuscript. Comment 16. Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency? Answer to the comment: The alternative residue, PRO 136, forms a hydrogen bond interaction with the core of IMID2. Comment 17. How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations? Answer to the comment: The docking score gives an idea about the score of a compound and is used to predict the binding affinity of protein and ligand when it is subjected to a molecular docking study. Qikprop gives an idea about the compound's ability to cross the BBB or not. The MM-GBSA gives an idea about the sum of intermolecular interactions present between the protein and ligand. Comment 18. What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process? Answer to the comment: In the present study, a molecular docking study was used to subject these compounds to Qikprop, then the compounds which cross the standard range of physicochemical properties. Those compounds were segregated based on VAL135 at N-H group followed by subjecting those compounds to Prime MM-GBSA and Molecular dynamic simulation studies. Comment 19. How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity? Answer to the comment: The identified compounds are better compared to the existing inhibitors of 6Y9R. Comment 20. Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding? Answer to the comment: The interactions during the MD simulation were stable for a larger period, hence, the shortlisted molecules can be better GSK-3β inhibitors. Comment 21. How do you plan to prioritize the compounds for further experimental testing, given the computational predictions? Answer to the comment: Yes, the compounds will be further subjected to in vitro studies and later to in vivo studies. Competing Interests: Authors declare that there is no competing interest. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 08 Jul 2024 Views 0 Cite How to cite this report: Bhatt S. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302514 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302514 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 10 Oct 2024 Shvetank Bhatt , Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.159332.r302514 The manuscript is well written and can be accepted after minor modifications. 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. 2. Results and discussion section is ... Continue reading READ ALL The manuscript is well written and can be accepted after minor modifications. 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. 2. Results and discussion section is presented well by authors. 3. Author should discuss the epidemiology and pathophysiology of diseases briefly to understand the severity of disease. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: CNS Diosrders, AD, Depression, Anxiety I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bhatt S. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302514 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302514 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 25 Jun 2025 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 25 Jun 2025 Author Response Comment 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. Answer to the comment: The aducanumab, a medication based ... Continue reading Comment 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. Answer to the comment: The aducanumab, a medication based on the Aβ theory, in 2021 FDA approved for the treatment of AD. Lecanemab's effectiveness and safety in treating early-stage AD require longer study trials. Comment 2. Results and discussion section is presented well by authors. Answer to the comment: We thank the reviewer. Comment 3. Author should discuss the epidemiology and pathophysiology of diseases briefly to understand the severity of disease. Answer to the comment: AD is the leading cause of dementia among the elderly, with an estimated 44 million individuals affected globally, a number projected to double by 2050. In the U.S., over 5.5 million are currently diagnosed. The pathophysiology of AD includes several key features: the presence of amyloid plaques, which disrupt neuronal communication and incite inflammatory responses; neurofibrillary tangles formed by hyperphosphorylated tau protein, leading to neuronal dysfunction and cell death; and neuroinflammation, where activated glial cells further damage neurons and exacerbate the disease's progression, cognitive decline and behavioral changes. Comment 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. Answer to the comment: The aducanumab, a medication based on the Aβ theory, in 2021 FDA approved for the treatment of AD. Lecanemab's effectiveness and safety in treating early-stage AD require longer study trials. Comment 2. Results and discussion section is presented well by authors. Answer to the comment: We thank the reviewer. Comment 3. Author should discuss the epidemiology and pathophysiology of diseases briefly to understand the severity of disease. Answer to the comment: AD is the leading cause of dementia among the elderly, with an estimated 44 million individuals affected globally, a number projected to double by 2050. In the U.S., over 5.5 million are currently diagnosed. The pathophysiology of AD includes several key features: the presence of amyloid plaques, which disrupt neuronal communication and incite inflammatory responses; neurofibrillary tangles formed by hyperphosphorylated tau protein, leading to neuronal dysfunction and cell death; and neuroinflammation, where activated glial cells further damage neurons and exacerbate the disease's progression, cognitive decline and behavioral changes. Competing Interests: Authors declare that there is no competing interest. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 25 Jun 2025 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 25 Jun 2025 Author Response Comment 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. Answer to the comment: The aducanumab, a medication based ... Continue reading Comment 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. Answer to the comment: The aducanumab, a medication based on the Aβ theory, in 2021 FDA approved for the treatment of AD. Lecanemab's effectiveness and safety in treating early-stage AD require longer study trials. Comment 2. Results and discussion section is presented well by authors. Answer to the comment: We thank the reviewer. Comment 3. Author should discuss the epidemiology and pathophysiology of diseases briefly to understand the severity of disease. Answer to the comment: AD is the leading cause of dementia among the elderly, with an estimated 44 million individuals affected globally, a number projected to double by 2050. In the U.S., over 5.5 million are currently diagnosed. The pathophysiology of AD includes several key features: the presence of amyloid plaques, which disrupt neuronal communication and incite inflammatory responses; neurofibrillary tangles formed by hyperphosphorylated tau protein, leading to neuronal dysfunction and cell death; and neuroinflammation, where activated glial cells further damage neurons and exacerbate the disease's progression, cognitive decline and behavioral changes. Comment 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. Answer to the comment: The aducanumab, a medication based on the Aβ theory, in 2021 FDA approved for the treatment of AD. Lecanemab's effectiveness and safety in treating early-stage AD require longer study trials. Comment 2. Results and discussion section is presented well by authors. Answer to the comment: We thank the reviewer. Comment 3. Author should discuss the epidemiology and pathophysiology of diseases briefly to understand the severity of disease. Answer to the comment: AD is the leading cause of dementia among the elderly, with an estimated 44 million individuals affected globally, a number projected to double by 2050. In the U.S., over 5.5 million are currently diagnosed. The pathophysiology of AD includes several key features: the presence of amyloid plaques, which disrupt neuronal communication and incite inflammatory responses; neurofibrillary tangles formed by hyperphosphorylated tau protein, leading to neuronal dysfunction and cell death; and neuroinflammation, where activated glial cells further damage neurons and exacerbate the disease's progression, cognitive decline and behavioral changes. Competing Interests: Authors declare that there is no competing interest. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Shah JS. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302510 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302510 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 13 Sep 2024 Jigna Samir Shah , Nirma University, Ahmedabad, Gujarat, India Approved VIEWS 0 https://doi.org/10.5256/f1000research.159332.r302510 1. Abstract can be modified. Also, in conclusion no specific compound was identified. 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. 3. Why ... Continue reading READ ALL 1. Abstract can be modified. Also, in conclusion no specific compound was identified. 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3β inhibition might be possible through different binding sites. If possible, it can also be explored. 4. There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using “we”, the authors can used indirect speech. 5. Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used. 6. In result sections, “Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.” is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood. 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter. 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration. 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies. 10. Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Neurodegenerative diseases, oral cancer, breast cancer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Shah JS. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302510 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302510 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 25 Jun 2025 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 25 Jun 2025 Author Response Comment 1. Abstract can be modified. Also, in conclusion no specific compound was identified. Answer to the comment: Thank you for your comment. The abstract and the conclusion have been ... Continue reading Comment 1. Abstract can be modified. Also, in conclusion no specific compound was identified. Answer to the comment: Thank you for your comment. The abstract and the conclusion have been modified according to the reviewer's comments in the revised manuscript. Comment 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. Answer to the comment: The relevant details were incorporated as per the reviewer's comment in the revised manuscript, page number: 10 Comment 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3β inhibition might be possible through different binding sites. If possible, it can also be explored. Answer to the comment: The PDB ID 6Y9R was released in 2020 for the protein GSK-3β, the amino acid sequence length is proper without any break in sequence, with good resolution and Ramachandran outliers. The SiteMap tool is generally used to explore the different binding sites of a protein. Based on the results from the SiteMap tool, the ATP binding site was explored to identify ATP-competitive inhibitors to explore it in Alzheimer's disease. Comment 4. There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using “we”, the authors can used indirect speech. Answer to the comment: These errors are corrected in the revised manuscript. The authors are thankful to the reviewer for the suggestions. Comment 5. Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used. Answer to the comment: These are taken care of in the revised manuscript. Comment 6. In result sections, “Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.” is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood. Answer to the comment: According to the reviewer's comment, the data was added in the revised manuscript, see page number 12. Comment 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter. Answer to the comment: The data was added in the revised manuscript according to the revised manuscript page number: 10-11. Comment 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration. Answer to the comment: The standard drug or inhibitor are not used for docking a there are no currently available approved drug acting through GSK-3β. Comment 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies. Answer to the comment: In the revised manuscript, these points are incorporated as per the reviewer’s comments. Comment 10. Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study. Answer to the comment: This point was taken care of in the revised manuscript. The shortlisted compounds can be subjected to in vitro studies to check the cytotoxicity, and based on those study results, the compound can be studied in an animal model to check the safety and efficacy. However, there are limitations to purchasing. The compound, based on the body weight of the animal, requires a larger amount of compound. Further, for synthesis, it needed a lot of time to get the correct scaffold and the required structure. Comment 1. Abstract can be modified. Also, in conclusion no specific compound was identified. Answer to the comment: Thank you for your comment. The abstract and the conclusion have been modified according to the reviewer's comments in the revised manuscript. Comment 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. Answer to the comment: The relevant details were incorporated as per the reviewer's comment in the revised manuscript, page number: 10 Comment 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3β inhibition might be possible through different binding sites. If possible, it can also be explored. Answer to the comment: The PDB ID 6Y9R was released in 2020 for the protein GSK-3β, the amino acid sequence length is proper without any break in sequence, with good resolution and Ramachandran outliers. The SiteMap tool is generally used to explore the different binding sites of a protein. Based on the results from the SiteMap tool, the ATP binding site was explored to identify ATP-competitive inhibitors to explore it in Alzheimer's disease. Comment 4. There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using “we”, the authors can used indirect speech. Answer to the comment: These errors are corrected in the revised manuscript. The authors are thankful to the reviewer for the suggestions. Comment 5. Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used. Answer to the comment: These are taken care of in the revised manuscript. Comment 6. In result sections, “Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.” is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood. Answer to the comment: According to the reviewer's comment, the data was added in the revised manuscript, see page number 12. Comment 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter. Answer to the comment: The data was added in the revised manuscript according to the revised manuscript page number: 10-11. Comment 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration. Answer to the comment: The standard drug or inhibitor are not used for docking a there are no currently available approved drug acting through GSK-3β. Comment 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies. Answer to the comment: In the revised manuscript, these points are incorporated as per the reviewer’s comments. Comment 10. Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study. Answer to the comment: This point was taken care of in the revised manuscript. The shortlisted compounds can be subjected to in vitro studies to check the cytotoxicity, and based on those study results, the compound can be studied in an animal model to check the safety and efficacy. However, there are limitations to purchasing. The compound, based on the body weight of the animal, requires a larger amount of compound. Further, for synthesis, it needed a lot of time to get the correct scaffold and the required structure. Competing Interests: Authors declare that there is no competing interest. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 25 Jun 2025 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 25 Jun 2025 Author Response Comment 1. Abstract can be modified. Also, in conclusion no specific compound was identified. Answer to the comment: Thank you for your comment. The abstract and the conclusion have been ... Continue reading Comment 1. Abstract can be modified. Also, in conclusion no specific compound was identified. Answer to the comment: Thank you for your comment. The abstract and the conclusion have been modified according to the reviewer's comments in the revised manuscript. Comment 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. Answer to the comment: The relevant details were incorporated as per the reviewer's comment in the revised manuscript, page number: 10 Comment 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3β inhibition might be possible through different binding sites. If possible, it can also be explored. Answer to the comment: The PDB ID 6Y9R was released in 2020 for the protein GSK-3β, the amino acid sequence length is proper without any break in sequence, with good resolution and Ramachandran outliers. The SiteMap tool is generally used to explore the different binding sites of a protein. Based on the results from the SiteMap tool, the ATP binding site was explored to identify ATP-competitive inhibitors to explore it in Alzheimer's disease. Comment 4. There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using “we”, the authors can used indirect speech. Answer to the comment: These errors are corrected in the revised manuscript. The authors are thankful to the reviewer for the suggestions. Comment 5. Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used. Answer to the comment: These are taken care of in the revised manuscript. Comment 6. In result sections, “Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.” is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood. Answer to the comment: According to the reviewer's comment, the data was added in the revised manuscript, see page number 12. Comment 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter. Answer to the comment: The data was added in the revised manuscript according to the revised manuscript page number: 10-11. Comment 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration. Answer to the comment: The standard drug or inhibitor are not used for docking a there are no currently available approved drug acting through GSK-3β. Comment 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies. Answer to the comment: In the revised manuscript, these points are incorporated as per the reviewer’s comments. Comment 10. Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study. Answer to the comment: This point was taken care of in the revised manuscript. The shortlisted compounds can be subjected to in vitro studies to check the cytotoxicity, and based on those study results, the compound can be studied in an animal model to check the safety and efficacy. However, there are limitations to purchasing. The compound, based on the body weight of the animal, requires a larger amount of compound. Further, for synthesis, it needed a lot of time to get the correct scaffold and the required structure. Comment 1. Abstract can be modified. Also, in conclusion no specific compound was identified. Answer to the comment: Thank you for your comment. The abstract and the conclusion have been modified according to the reviewer's comments in the revised manuscript. Comment 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. Answer to the comment: The relevant details were incorporated as per the reviewer's comment in the revised manuscript, page number: 10 Comment 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3β inhibition might be possible through different binding sites. If possible, it can also be explored. Answer to the comment: The PDB ID 6Y9R was released in 2020 for the protein GSK-3β, the amino acid sequence length is proper without any break in sequence, with good resolution and Ramachandran outliers. The SiteMap tool is generally used to explore the different binding sites of a protein. Based on the results from the SiteMap tool, the ATP binding site was explored to identify ATP-competitive inhibitors to explore it in Alzheimer's disease. Comment 4. There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using “we”, the authors can used indirect speech. Answer to the comment: These errors are corrected in the revised manuscript. The authors are thankful to the reviewer for the suggestions. Comment 5. Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used. Answer to the comment: These are taken care of in the revised manuscript. Comment 6. In result sections, “Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.” is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood. Answer to the comment: According to the reviewer's comment, the data was added in the revised manuscript, see page number 12. Comment 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter. Answer to the comment: The data was added in the revised manuscript according to the revised manuscript page number: 10-11. Comment 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration. Answer to the comment: The standard drug or inhibitor are not used for docking a there are no currently available approved drug acting through GSK-3β. Comment 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies. Answer to the comment: In the revised manuscript, these points are incorporated as per the reviewer’s comments. Comment 10. Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study. Answer to the comment: This point was taken care of in the revised manuscript. The shortlisted compounds can be subjected to in vitro studies to check the cytotoxicity, and based on those study results, the compound can be studied in an animal model to check the safety and efficacy. However, there are limitations to purchasing. The compound, based on the body weight of the animal, requires a larger amount of compound. Further, for synthesis, it needed a lot of time to get the correct scaffold and the required structure. Competing Interests: Authors declare that there is no competing interest. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Zhao Q. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302509 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302509 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Jul 2024 Qingchun Zhao , Shenyang Pharmaceutical University, Shenyang, China Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.159332.r302509 In this paper, the authors used computer simulation technology to select 9 compounds with GSK3 as the target, and then studied ADME prediction and molecular docking through different modules. Keep reading the paper, three questions are raised. 1. ... Continue reading READ ALL In this paper, the authors used computer simulation technology to select 9 compounds with GSK3 as the target, and then studied ADME prediction and molecular docking through different modules. Keep reading the paper, three questions are raised. 1. In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification. 2. In the introduction section, is there only one sentence to summarize the article? 3.The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article. Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Alzheimer's Disease, Cancer , Natural Pharmaceutical Chemistry, Pharmaceutical chemistry I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Zhao Q. Reviewer Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302509 ) The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302509 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 27 Aug 2024 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 27 Aug 2024 Author Response Reviewer Comment 1: In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, ... Continue reading Reviewer Comment 1: In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification. Response to comment 1 : We used Maestro, a graphical interface of Schrodinger, for our computational simulation studies. In the present work, we have used different modules such as Protein Preparation Wizard for Protein Preparation, LigPrep for Ligand Preparation, Qikprop for ADME (Absorption, Distribution, Metabolism and Excretion) prediction, Glide for docking studies, Prime for Binding energy prediction and Desmond for Molecular dynamic simulation studies used. Reviewer Comment 2 : In the introduction section, is there only one sentence to summarize the article? Response to comment 2 : For better BBB permeation, the structure was finalized based on a wet lab. So, based on this literature search, we have selected core imidazole scaffold for our study, and from that core imidazole scaffold, we have drawn sub-structures in the enamine database. Then docking studies were done on the compounds and subjected to ADME (Qikprop). A molecular dynamic simulation study was conducted on nine compounds. Reviewer Comment 3 : The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article. Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive. Response to comment 3 : The reviewer's comment is completely one-sided. We regret to answer this comment, and we completely disagree with the reviewer's suggestion, as this work took 18 months from the conceptualization of the idea. This work is computational modelling, and our objectives are clear, and in our view, the data generated is sufficient to make the conclusion. Reviewer Comment 1: In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification. Response to comment 1 : We used Maestro, a graphical interface of Schrodinger, for our computational simulation studies. In the present work, we have used different modules such as Protein Preparation Wizard for Protein Preparation, LigPrep for Ligand Preparation, Qikprop for ADME (Absorption, Distribution, Metabolism and Excretion) prediction, Glide for docking studies, Prime for Binding energy prediction and Desmond for Molecular dynamic simulation studies used. Reviewer Comment 2 : In the introduction section, is there only one sentence to summarize the article? Response to comment 2 : For better BBB permeation, the structure was finalized based on a wet lab. So, based on this literature search, we have selected core imidazole scaffold for our study, and from that core imidazole scaffold, we have drawn sub-structures in the enamine database. Then docking studies were done on the compounds and subjected to ADME (Qikprop). A molecular dynamic simulation study was conducted on nine compounds. Reviewer Comment 3 : The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article. Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive. Response to comment 3 : The reviewer's comment is completely one-sided. We regret to answer this comment, and we completely disagree with the reviewer's suggestion, as this work took 18 months from the conceptualization of the idea. This work is computational modelling, and our objectives are clear, and in our view, the data generated is sufficient to make the conclusion. Competing Interests: NIL Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 27 Aug 2024 Sreedhara Ranganath Pai , Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India 27 Aug 2024 Author Response Reviewer Comment 1: In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, ... Continue reading Reviewer Comment 1: In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification. Response to comment 1 : We used Maestro, a graphical interface of Schrodinger, for our computational simulation studies. In the present work, we have used different modules such as Protein Preparation Wizard for Protein Preparation, LigPrep for Ligand Preparation, Qikprop for ADME (Absorption, Distribution, Metabolism and Excretion) prediction, Glide for docking studies, Prime for Binding energy prediction and Desmond for Molecular dynamic simulation studies used. Reviewer Comment 2 : In the introduction section, is there only one sentence to summarize the article? Response to comment 2 : For better BBB permeation, the structure was finalized based on a wet lab. So, based on this literature search, we have selected core imidazole scaffold for our study, and from that core imidazole scaffold, we have drawn sub-structures in the enamine database. Then docking studies were done on the compounds and subjected to ADME (Qikprop). A molecular dynamic simulation study was conducted on nine compounds. Reviewer Comment 3 : The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article. Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive. Response to comment 3 : The reviewer's comment is completely one-sided. We regret to answer this comment, and we completely disagree with the reviewer's suggestion, as this work took 18 months from the conceptualization of the idea. This work is computational modelling, and our objectives are clear, and in our view, the data generated is sufficient to make the conclusion. Reviewer Comment 1: In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification. Response to comment 1 : We used Maestro, a graphical interface of Schrodinger, for our computational simulation studies. In the present work, we have used different modules such as Protein Preparation Wizard for Protein Preparation, LigPrep for Ligand Preparation, Qikprop for ADME (Absorption, Distribution, Metabolism and Excretion) prediction, Glide for docking studies, Prime for Binding energy prediction and Desmond for Molecular dynamic simulation studies used. Reviewer Comment 2 : In the introduction section, is there only one sentence to summarize the article? Response to comment 2 : For better BBB permeation, the structure was finalized based on a wet lab. So, based on this literature search, we have selected core imidazole scaffold for our study, and from that core imidazole scaffold, we have drawn sub-structures in the enamine database. Then docking studies were done on the compounds and subjected to ADME (Qikprop). A molecular dynamic simulation study was conducted on nine compounds. Reviewer Comment 3 : The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article. Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive. Response to comment 3 : The reviewer's comment is completely one-sided. We regret to answer this comment, and we completely disagree with the reviewer's suggestion, as this work took 18 months from the conceptualization of the idea. This work is computational modelling, and our objectives are clear, and in our view, the data generated is sufficient to make the conclusion. Competing Interests: NIL Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 08 Jul 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 3 (revision) 27 May 25 Version 2 (revision) 27 Aug 24 read Version 1 08 Jul 24 read read read Qingchun Zhao , Shenyang Pharmaceutical University, Shenyang, China Jigna Samir Shah , Nirma University, Ahmedabad, India Sachchida Rai , Banaras Hindu University, Varanasi, India Payal Singh , Banaras Hindu University, Varanasi, India Shvetank Bhatt , Dr. Vishwanath Karad MIT World Peace University, Pune, India Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Rai S et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 28 Sep 2024 | for Version 2 Sachchida Rai , Centre of Experimental Medicine and Surgery (CEMS), Institute of Medical Sciences (IMS), Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India Payal Singh , Department of Zoology, Banaras Hindu University, Varanasi, Uttar Pradesh, India 0 Views copyright © 2024 Rai S et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I have some questions on the manuscript which are as follows. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy? Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered? How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies? Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study? Have you considered testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models? Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time? How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline? Was there any off-target effects observed for the identified inhibitors in the computational analyses? Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), & Rai SN, et al., 2020 (Ref 6) in your manuscript. Could targeting GSK-3β alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach? How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R? What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity? How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation? Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes? Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency? How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations? What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process? How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity? Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding? How do you plan to prioritize the compounds for further experimental testing, given the computational predictions? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Ramakrishna K, Karuturi P, Siakabinga Q, T A G, et al.: Indole-3 Carbinol and Diindolylmethane Mitigated β-Amyloid-Induced Neurotoxicity and Acetylcholinesterase Enzyme Activity: In Silico, In Vitro, and Network Pharmacology Study. Diseases . 2024; 12 (8). PubMed Abstract | Publisher Full Text 2. Tripathi PN, Lodhi A, Rai SN, Nandi NK, et al.: Review of Pharmacotherapeutic Targets in Alzheimer's Disease and Its Management Using Traditional Medicinal Plants. Degener Neurol Neuromuscul Dis . 2024; 14 : 47-74 PubMed Abstract | Publisher Full Text 3. Singh M, Agarwal V, Pancham P, Jindal D, et al.: A Comprehensive Review and Androgen Deprivation Therapy and Its Impact on Alzheimer's Disease Risk in Older Men with Prostate Cancer. Degener Neurol Neuromuscul Dis . 2024; 14 : 33-46 PubMed Abstract | Publisher Full Text 4. Tripathi PN, Srivastava P, Sharma P, Tripathi MK, et al.: Biphenyl-3-oxo-1,2,4-triazine linked piperazine derivatives as potential cholinesterase inhibitors with anti-oxidant property to improve the learning and memory. Bioorg Chem . 2019; 85 : 82-96 PubMed Abstract | Publisher Full Text 5. Srivastava P, Tripathi PN, Sharma P, Rai SN, et al.: Design and development of some phenyl benzoxazole derivatives as a potent acetylcholinesterase inhibitor with antioxidant property to enhance learning and memory. Eur J Med Chem . 2019; 163 : 116-135 PubMed Abstract | Publisher Full Text 6. Rai SN, Singh C, Singh A, Singh MP, et al.: Mitochondrial Dysfunction: a Potential Therapeutic Target to Treat Alzheimer's Disease. Mol Neurobiol . 2020; 57 (7): 3075-3088 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise My research focuses on exploring the molecular mechanisms and therapeutic interventions for neurodegenerative diseases, particularly Alzheimer's and Parkinson's disease. I investigate the roles of biomarkers, signaling pathways, and epigenetic regulators in disease progression. My work integrates computational biology, drug discovery, and experimental models to identify novel therapeutic targets, including GSK-3β and other key enzymes. I also explore natural compounds, such as those derived from nutraceuticals and endophytic fungi, for their neuroprotective potential. Additionally, I study autophagy, oxidative stress, and inflammation as critical factors in neurodegeneration, aiming to develop targeted strategies for chronic disease mitigation and brain health. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 16 Jun 2025 Sreedhara Ranganath Pai, Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India Comment 1. How do the identified GSK-3β inhibitors compare to existing therapeutics in terms of binding affinity and specificity? Answer to the comment: The binding affinity and specificity are generally related in that high binding affinity usually leads to high specificity for a particular target. The binding affinity and specificity of GSK-3β inhibitors vary depending on the type of inhibitors/ lignads used. Comment 2. What specific modifications were made to the previously used computational methods, and how did they enhance the prediction accuracy? Answer to the comment: Instead of a similarity structure in this study, substructure search was used for IMID2 scaffold at Enamine and the compounds were subjected to Qikprop to predict the accuracy of the compounds to cross the BBB (QPlogBB). In a previous study, QPlogBB was not used, whereas other physicochemical parameters were used, such as molecular weight, hydrogen bond donor and hydrogen bond acceptor. Comment 3. Can you provide more details on the ligand candidates identified? Were any novel chemical structures or scaffolds discovered? Answer to the comment: In this study, the substructure compound search was used for ligand identification. All the compounds used in this study were novel, but scaffold was similar to previous literature. Comment 4. How do the ADME predictions of the identified ligands suggest their potential success in in vivo studies? Answer to the comment: Absorption is how the drugs go through the organs of the body to reach the systemic circulation. Distribution of the drug/compound transported from one tissue to another tissue or from one organ to another organ. The transportation or distribution of compounds/drugs into the brain/CNS is the main focus in drug discovery. The metabolism is also referred to as the biotransformation of exogenous compounds/drugs to increase their water solubility and hydrophilicity. Finally, the water solubility facilitates their excretion process. Using the information to evaluate the compound's drug safety and risk outcomes. Comment 5. Were there any challenges encountered in using the computational simulation tools, and how were they addressed in this study? Answer to the comment: As the computation methods are regularly and routinely used, there are no challenges encountered in using the computational simulation tools. Comment 6. Have you considered testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models? Answer to the comment: No, yet to consider testing the identified GSK-3β inhibitors in relevant Alzheimer’s disease animal models. Comment 7. Did the molecular dynamics simulations provide insights into the stability and conformational changes of the protein-ligand complexes over time? Answer to the comment: Yes, the molecular dynamics (MD) simulations can provide insights into the stability and conformational changes of protein-ligand complexes over time. MD simulation studies predict how protein-ligand interactions occur, analyse dynamic changes and conformational changes in the proteins. Comment 8. How do you plan to validate the computational results experimentally, and what are the next steps in your drug development pipeline? Answer to the comment: The finalized compounds will be subjected to in vitro and in vivo studies. If the molecules are showing good results, they can be translated into clinical studies. Comment 9. Was there any off-target effects observed for the identified inhibitors in the computational analyses? Answer to the comment: No such off-target effects were observed for inhibitors in computational analyses using the Schrodinger Maestro tool. Comment 10. Include some relevant bibliographic studies like Ramakrishna K, et al., 2024 (Ref 1), Tripathi PN, et al., 2024 (Ref 2), Singh M, et al., 2024 (Ref 3), Tripathi PN, et al., 2019 (Ref 4), Srivastava P, et al., 2019 (Ref 5), & Rai SN, et al., 2020 (Ref 6) in your manuscript. Answer to the comment: These references are not related to our objectives, hence these are not included as relevant references. Comment 11. Could targeting GSK-3β alone be sufficient to address the complex pathology of Alzheimer's disease, or do you propose a combination therapy approach? Answer to the comment: As most of the current study focuses on targeting GSK-3β, and the preclinical data to a greater extent prevent Alzheimer’s disease, however, the question of whether GSK-3β alone is sufficient to address the complex pathology of Alzheimer's disease remains to be answered through clinical investigations. Comment 12. How did the focused library generation improve the targeting accuracy of the ATP-competitive site in 6Y9R? Answer to the comment: The identified GSK-3β inhibitors particularly bind to the specific ATP-competitive site in 6Y9R protein region and reduce the off-target effects. Comment 13. What criteria were used to select compounds based on the VAL135 residue interaction, and how critical is this interaction for inhibitory activity? Answer to the comment: This was based on previous literature, in this study N-region of the core and VAL135 at N-H group of the ligand. Comment 14. How reliable are the Qikprop and Prime MM-GBSA assays in predicting the efficacy of the compounds before experimental validation? Answer to the comment: Qikprop: Predicts pharmaceutically relevant properties of the compounds and physically significant descriptors of an individual compound. Prime MM-GBSA: Predicts the protein-ligand binding free energy of an individual compound. This was accurate to estimate the relative binding free energy of the compound/ligand. Comment 15. Can you elaborate on the molecular dynamics simulation results, specifically regarding the stability of the protein-ligand complexes? Answer to the comment: We have elaborated this part in the revised manuscript. Comment 16. Were any alternative residues besides VAL135 considered for interaction, and how might they impact the binding efficiency? Answer to the comment: The alternative residue, PRO 136, forms a hydrogen bond interaction with the core of IMID2. Comment 17. How were the docking scores correlated with the Qikprop predictions and MM-GBSA binding energy calculations? Answer to the comment: The docking score gives an idea about the score of a compound and is used to predict the binding affinity of protein and ligand when it is subjected to a molecular docking study. Qikprop gives an idea about the compound's ability to cross the BBB or not. The MM-GBSA gives an idea about the sum of intermolecular interactions present between the protein and ligand. Comment 18. What experimental techniques do you propose to validate the computational findings, and what challenges do you anticipate in this process? Answer to the comment: In the present study, a molecular docking study was used to subject these compounds to Qikprop, then the compounds which cross the standard range of physicochemical properties. Those compounds were segregated based on VAL135 at N-H group followed by subjecting those compounds to Prime MM-GBSA and Molecular dynamic simulation studies. Comment 19. How do the identified compounds compare to existing inhibitors of 6Y9R in terms of predicted binding affinity and selectivity? Answer to the comment: The identified compounds are better compared to the existing inhibitors of 6Y9R. Comment 20. Did the molecular dynamics simulations reveal any significant conformational changes in the receptor that might affect ligand binding? Answer to the comment: The interactions during the MD simulation were stable for a larger period, hence, the shortlisted molecules can be better GSK-3β inhibitors. Comment 21. How do you plan to prioritize the compounds for further experimental testing, given the computational predictions? Answer to the comment: Yes, the compounds will be further subjected to in vitro studies and later to in vivo studies. View more View less Competing Interests Authors declare that there is no competing interest. reply Respond Report a concern Rai S and Singh P. Peer Review Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.170347.r318530) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-773/v2#referee-response-318530 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Bhatt S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 10 Oct 2024 | for Version 1 Shvetank Bhatt , Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India 0 Views copyright © 2024 Bhatt S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The manuscript is well written and can be accepted after minor modifications. 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. 2. Results and discussion section is presented well by authors. 3. Author should discuss the epidemiology and pathophysiology of diseases briefly to understand the severity of disease. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise CNS Diosrders, AD, Depression, Anxiety I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 25 Jun 2025 Sreedhara Ranganath Pai, Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India Comment 1. Author can highlight the names of drugs which are recently approved for the treatment of AD in introduction section. Answer to the comment: The aducanumab, a medication based on the Aβ theory, in 2021 FDA approved for the treatment of AD. Lecanemab's effectiveness and safety in treating early-stage AD require longer study trials. Comment 2. Results and discussion section is presented well by authors. Answer to the comment: We thank the reviewer. Comment 3. Author should discuss the epidemiology and pathophysiology of diseases briefly to understand the severity of disease. Answer to the comment: AD is the leading cause of dementia among the elderly, with an estimated 44 million individuals affected globally, a number projected to double by 2050. In the U.S., over 5.5 million are currently diagnosed. The pathophysiology of AD includes several key features: the presence of amyloid plaques, which disrupt neuronal communication and incite inflammatory responses; neurofibrillary tangles formed by hyperphosphorylated tau protein, leading to neuronal dysfunction and cell death; and neuroinflammation, where activated glial cells further damage neurons and exacerbate the disease's progression, cognitive decline and behavioral changes. View more View less Competing Interests Authors declare that there is no competing interest. reply Respond Report a concern Bhatt S. Peer Review Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302514) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302514 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Shah J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Sep 2024 | for Version 1 Jigna Samir Shah , Nirma University, Ahmedabad, Gujarat, India 0 Views copyright © 2024 Shah J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Abstract can be modified. Also, in conclusion no specific compound was identified. 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3β inhibition might be possible through different binding sites. If possible, it can also be explored. 4. There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using “we”, the authors can used indirect speech. 5. Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used. 6. In result sections, “Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.” is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood. 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter. 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration. 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies. 10. Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Neurodegenerative diseases, oral cancer, breast cancer I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 25 Jun 2025 Sreedhara Ranganath Pai, Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India Comment 1. Abstract can be modified. Also, in conclusion no specific compound was identified. Answer to the comment: Thank you for your comment. The abstract and the conclusion have been modified according to the reviewer's comments in the revised manuscript. Comment 2. Role of GSK-3β in pathogenesis of AD can be explored more. Mechanism of GSK-3β in AD can be defined briefly in introduction section. Answer to the comment: The relevant details were incorporated as per the reviewer's comment in the revised manuscript, page number: 10 Comment 3. Why this PDB ID 6Y9R was selected from 89 IDs as mentioned in results and discussion. It can be elaborated briefly. Also, besides this domain, GSK-3β inhibition might be possible through different binding sites. If possible, it can also be explored. Answer to the comment: The PDB ID 6Y9R was released in 2020 for the protein GSK-3β, the amino acid sequence length is proper without any break in sequence, with good resolution and Ramachandran outliers. The SiteMap tool is generally used to explore the different binding sites of a protein. Based on the results from the SiteMap tool, the ATP binding site was explored to identify ATP-competitive inhibitors to explore it in Alzheimer's disease. Comment 4. There are grammatical errors and improper sentence formations in manuscript, which can be modified. Also, instead of using “we”, the authors can used indirect speech. Answer to the comment: These errors are corrected in the revised manuscript. The authors are thankful to the reviewer for the suggestions. Comment 5. Full forms mentioned are not consistent. Mention the full form in first use and thereafter abbreviations can be used. Answer to the comment: These are taken care of in the revised manuscript. Comment 6. In result sections, “Protein root mean square fluctuation (RMSF) is mostly helpful for predicting changes that occur locally along the enzyme chain.” is repeatedly mentioned in each result. The authors can mention the significance of findings like RMSD, RMSF at the start in a separate paragraph, or only once in initial results. Thereafter it can be understood. Answer to the comment: According to the reviewer's comment, the data was added in the revised manuscript, see page number 12. Comment 7. In ADME studies, it is mentioned that QPlogBB (predicted brain/blood partition coefficient), was predicted however, in results it is not mentioned. It can be mentioned, to evaluate blood brain barrier permeability. In AD, since the target region for therapy is brain, BBB permeability is a significant parameter. Answer to the comment: The data was added in the revised manuscript according to the revised manuscript page number: 10-11. Comment 8. No standard drug or inhibitor is included in MDS studies. If possible, a standard can be taken into consideration. Answer to the comment: The standard drug or inhibitor are not used for docking a there are no currently available approved drug acting through GSK-3β. Comment 9. There is no discussion about findings of the article, and conclusion is very vague. At the end of study, there is no comparison of the 9 compounds screened. Better compound should be identified and screened. Biological data to support the findings can be added, however it is not mandatory. Discussion section should be added and findings of each compound should be compared and correlated to previously done studies. Answer to the comment: In the revised manuscript, these points are incorporated as per the reviewer’s comments. Comment 10. Discuss future prospects of this study, elaborate briefly what experimental studies should be done further and state the limitations of this study. Answer to the comment: This point was taken care of in the revised manuscript. The shortlisted compounds can be subjected to in vitro studies to check the cytotoxicity, and based on those study results, the compound can be studied in an animal model to check the safety and efficacy. However, there are limitations to purchasing. The compound, based on the body weight of the animal, requires a larger amount of compound. Further, for synthesis, it needed a lot of time to get the correct scaffold and the required structure. View more View less Competing Interests Authors declare that there is no competing interest. reply Respond Report a concern Shah JS. Peer Review Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302510) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302510 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Zhao Q. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Jul 2024 | for Version 1 Qingchun Zhao , Shenyang Pharmaceutical University, Shenyang, China 0 Views copyright © 2024 Zhao Q. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In this paper, the authors used computer simulation technology to select 9 compounds with GSK3 as the target, and then studied ADME prediction and molecular docking through different modules. Keep reading the paper, three questions are raised. 1. In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification. 2. In the introduction section, is there only one sentence to summarize the article? 3.The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article. Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Alzheimer's Disease, Cancer , Natural Pharmaceutical Chemistry, Pharmaceutical chemistry I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 27 Aug 2024 Sreedhara Ranganath Pai, Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, 576104, India Reviewer Comment 1: In the abstract, the author describes the method as "we have used different modules that were used in previous studies with a little modification", but in fact, the method used by the author is a routine application, and there is no prominent modification. Response to comment 1 : We used Maestro, a graphical interface of Schrodinger, for our computational simulation studies. In the present work, we have used different modules such as Protein Preparation Wizard for Protein Preparation, LigPrep for Ligand Preparation, Qikprop for ADME (Absorption, Distribution, Metabolism and Excretion) prediction, Glide for docking studies, Prime for Binding energy prediction and Desmond for Molecular dynamic simulation studies used. Reviewer Comment 2 : In the introduction section, is there only one sentence to summarize the article? Response to comment 2 : For better BBB permeation, the structure was finalized based on a wet lab. So, based on this literature search, we have selected core imidazole scaffold for our study, and from that core imidazole scaffold, we have drawn sub-structures in the enamine database. Then docking studies were done on the compounds and subjected to ADME (Qikprop). A molecular dynamic simulation study was conducted on nine compounds. Reviewer Comment 3 : The content of the whole study is simple, the innovation is poor, and the workload is less, which is not enough to support a research article. Therefore, it is hoped that the author can modify some contents of the article and add some follow-up experiments to make the article more comprehensive. Response to comment 3 : The reviewer's comment is completely one-sided. We regret to answer this comment, and we completely disagree with the reviewer's suggestion, as this work took 18 months from the conceptualization of the idea. This work is computational modelling, and our objectives are clear, and in our view, the data generated is sufficient to make the conclusion. View more View less Competing Interests NIL reply Respond Report a concern Zhao Q. Peer Review Report For: Molecular docking studies and molecular dynamic simulation analysis: To identify novel ATP-competitive inhibition of Glycogen synthase kinase-3β for Alzheimer’s disease [version 3; peer review: 2 approved, 1 approved with reservations, 1 not approved] . F1000Research 2025, 13 :773 ( https://doi.org/10.5256/f1000research.159332.r302509) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-773/v1#referee-response-302509 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Molecular docking studies and molecular dynamic...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/13-773/v3" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/13-773/v3&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/13-773/v3" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Shri SR et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/13-773/v3/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/13-773", templates : { twitter : "Molecular docking studies and molecular dynamic simulation analysis:.... Shri SR et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/13-773/v3" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/145391/182210") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "182210"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "322308": 0, "322309": 0, "322310": 0, "322311": 0, "322306": 0, "322307": 0, "322312": 0, "322313": 0, "322314": 0, "322315": 0, "302508": 0, "302509": 34, "302510": 23, "302511": 0, "302505": 0, "302506": 0, "302507": 0, "302512": 0, "302513": 0, "302514": 12, "318532": 0, "318533": 0, "318534": 0, "318535": 0, "318528": 0, "318529": 0, "318530": 41, "318531": 0, "318536": 0, "318537": 0, "311252": 0, "311253": 0, "311254": 0, "311255": 0, "311250": 0, "311251": 0, "311256": 0, "311257": 0, "311258": 0, "311259": 0, "325990": 0, "325991": 0, "325996": 0, "387437": 0, "325997": 0, "387436": 0, "325998": 0, "387439": 0, "325999": 0, "387438": 0, "325992": 0, "325993": 0, "325994": 0, "325995": 0, "317682": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "f7390ac3-7ccb-4526-b19a-ecc9018f92f7"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });