Identification and selection of potential TNF-α inhibitors as anti- encephalitis candidates by using in silico assisted drug design

Identification and selection of potential TNF-α inhibitors as anti- encephalitis candidates by using in silico assisted drug design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification and selection of potential TNF-α inhibitors as anti- encephalitis candidates by using in silico assisted drug design Pawan Kumar Gupta, Shashi Bhooshan Tiwari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6028232/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Encephalitis, an inflammatory disorder of the brain caused by infections or autoimmune responses, is still a major global health concern due to its high morbidity, mortality, and long-term neurological consequences. The available therapy alternatives are usually confined, and conventional drug discovery procedures are both resource-intensive and time-consuming. These challenges underscore the critical need for novel tools to speed up therapy development, which could revolutionize treatment regimens and enhance results for impacted people globally. Objective To identify, optimize & selection of TNF-α inhibitors as anti-encephalitis candidates by using in silico assisted drug design approach. Method Computer-aided drug design (CADD) is a revolutionize tool for identifying potential anti-encephalitis candidates through its ability to simulate molecular interactions, predict drug-target affinities, and optimize pharmacokinetic properties with molecular dynamics simulation. In this study, we have employed comprehensive in silico method to find effective therapeutic compounds that targets the TNF-α receptor in encephalitis. Result An in silico screening of nearly 600 isatin derivatives were conducted by using data from the PubChem database. It was carried out using the Lipinski rule of five in addition to other criteria. After filtering, 48 Isatin derivatives were selected, and six compounds with binding affinities exceeding − 8.0 kcal/mol were identified as potential candidates. Additionally it was subjected to ADME analysis using Swiss ADME software. Every contender demonstrated greater binding affinity and actively crossed the BBB. Protein-ligand complexes were subjected to molecular dynamics (MD) simulations using CABS-flexV2.0 and the iMOD server in order to assess the root-mean-square fluctuations (RMSFs) and quantify protein stability respectively. Conclusion Hence, it is concluded that compounds with higher binding affinity and actively cross BBB values showed effective anti-encephalitis agents. We have performed molecular docking studies, ADMET analysis and MD simulation of all selected compounds and found that compounds G5, G17, G48, G15, G3 and G18 have showed better binding score against TNF-α in contrast to standard drug Acyclovir. TNF-α molecular docking anti-encephalitis Isatin CADD Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 1. Introduction A serious neurological condition called encephalitis is characterized by inflammation of the brain 1 and is frequently brought on by bacterial infections, viral infections, or autoimmune reactions 2 . Because of potential for long-term neurological consequences, and rising incidence in certain regions – particularly those where some areas where vector-borne encephalitis viruses like the Zika virus and Japanese encephalitis are endemic 3 , 4 . Currently, treatment options are limited to supportive care and broad-spectrum antivirals 5 , which often fail to effectively target specific causative agents or prevent long-term complications 6 , 7 . These challenges highlight the urgent need for innovative and targeted therapeutic approaches. CADD has emerged as a game changer in drug development, providing novel ways for effectively identifying and optimizing prospective therapeutic candidates 8 . Unlike traditional high-throughput screening methods, which are time and resource intensive 9 , CADD employs computational techniques to predict drug-target interactions, screen large compound libraries, and refine lead molecules for improved efficacy and safety 10 . The integration of these approaches has proven important in the quick identification of inhibitors against critical viral proteins, host factors, and inflammatory pathways involved in encephalitis 11 – 12 . The increasing use of artificial intelligence (AI) improves the predictive power of CADD, allowing researchers to predict complicated interactions 13 , identify potential off-target effects, and optimize drug-like features with remarkable accuracy. These innovations speed up drug development, especially for urgent situations like developing encephalitis epidemics driven by arboviruses or new other infections 14 – 15 . Furthermore, the use of virtual screening and molecular dynamics simulations enables compound prioritization for experimental validation 16 , lowering the cost and time associated with laboratory-based drug development 17 . Recent investigations have successfully used CADD in the development 18 of anti-encephalitis drugs, discovering promising inhibitors of viral polymerases, proteases, and envelope proteins, as well as modulators of host immune responses. Such approaches highlight CADD's ability to address critical issues in encephalitis therapy 19 , such as drug resistance and the necessity for pathogen-specific therapeutics. By merging computational tools with experimental procedures, CADD is a potent option for accelerating the development of innovative therapies to tackle this debilitating ailment 21 . The present study primarily focuses on Tumor Necrosis Factor-alpha (TNF-α), which is pro-inflammatory cytokine that plays a critical role in immune responses, cell survival, apoptosis, and inflammation. It binds to TNF receptors (TNFR1 and TNFR2), triggering intracellular signaling cascades like NF-κB and MAPK pathways, and leading to cytokine production, immune cell recruitment and inflammation 22 . Excessive TNF-α activity contributes to chronic inflammatory diseases such as rheumatoid arthritis, psoriasis, and Crohn’s disease. Inhibiting TNF-α helps mitigate pathological inflammation, preventing tissue damage and autoimmunity. Developing TNF-α inhibitors is crucial for controlling these conditions while balancing immune responses to avoid compromising host defense against infections and malignancies 23 . This research exclusively investigates isatin derivatives as primarily candidate. Isatin (1H-indole-2,3-dione) is a heterocyclic compound first discovered in 1841 as an oxidation product of indigo. Structurally, it consists of an indole core with keto functional groups at the second and third positions, forming a unique bicyclic system 24 . The presence of these functional groups enables isatin to act as a versatile scaffold in medicinal chemistry, facilitating interactions with biological targets. The structural diversity of isatin derivatives arises from modifications at the nitrogen atom or the aromatic ring, which significantly influences their pharmacological properties. Due to its ability to interact with various enzymes and receptors, isatin and its derivatives are extensively studied for their therapeutic potential. They exhibit a broad spectrum of biological activities, including antimicrobial, antiviral, anticancer, anticonvulsant, and anti-inflammatory effects 25 , 26 . Additionally, their antiviral potential has been explored against various viruses, including influenza and corona viruses, by targeting viral replication pathways 27 . The structural versatility and bioactivity of isatin makes it a valuable scaffold in drug discovery. Continued research into its derivatives may lead to the development of novel therapeutics with improved efficacy and selectivity against various diseases 28 . 2. Material and methods 2.1 Dataset generation: To construct a comprehensive dataset, ligand data groups were selected from the PubChem database based on their structural diversity. The dataset preparation process involved systematic retrieval, curation, and processing of chemical to support research objectives. Initially, suitable compounds are found using keyword searches, assay findings, or structural similarities to known ligands. Approximately 600 Isatin derivative chemicals were found in the PubChem database after conducting a literature search. From the PubChem database ( https://pubchem.ncbi.nlm.nih.gov ), a free online collection of chemicals, 48 isatin derivatives with decreased lethal activity and molecular weights smaller than 500 dalton were chosen using the Lipinski rule of five and new filtering techniques. All chemical structures were downloaded in .sdf format. The Swiss Dock tool 29 was used in Autodock vina mode to get ligands ready for docking studies. The resulting chemical structures were transferred to .pdb file format at the conclusion of the ligand production process. Table 1 list of 48 isatin derivatives with physicochemical parameters- S.No. Comp. ID Compound code Molecular formula Molecular weight(g/mol) HBA HBD Log P Value Violation TPSA (A 0 2 ) Number of Rotatable Bond 1 53399318 G1 C 10 H 9 NO 3 191.18 3 1 1.1 0 55.4 1 2 61356413 G2 C 16 H 13 NO 3 267.28 3 0 2.4 0 46.6 3 3 53319928 G3 C 16 H 13 NO 2 251.28 2 1 3 0 46.2 3 4 44603420 G4 C 13 H 15 BrN 2 O 2 311.17 3 0 2.5 0 40.6 4 5 44575103 G5 C 16 H 10 ClNO 2 283.71 2 1 3.7 0 46.2 2 6 129750961 G6 C 11 H 8 BrNO 2 S 298.16 3 0 2.1 0 62.7 1 7 129840580 G7 C 11 H 13 NO 2 211.18 4 0 0.4 0 85 1 8 53996225 G8 C 8 H 6 N 2 O 4 S 226.21 5 1 0 0 106 1 9 129750642 G9 C 11 H 9 NO 2 S 219.26 3 0 1.1 0 62.7 1 10 1390519551 G10 C 14 H 7 Br 2 NO 2 381.02 2 0 3.8 0 37.4 1 11 141296687 G11 C 9 H 5 NO 3 175.14 3 0 0 0 63.6 1 12 129636365 G12 C 10 H 10 N 2 O 2 190.2 3 1 0.9 0 63.4 0 13 70257034 G13 C 8 H 5 BrN 2 O 4 273.04 4 1 0.6 0 92 0 14 123242103 G14 C 10 H 11 NO 3 193.2 4 1 1.2 0 47.6 2 15 135487314 G15 C 15 H 10 ClN 3 O 283.71 3 1 3.3 0 53.8 2 16 129710558 G16 C 11 H 11 NO 5 237.21 5 0 0.9 0 65.1 3 17 44575104 G17 C 16 H 10 FNO 2 267.25 3 1 3.2 0 46.2 2 18 53322432 G18 C 16 H 13 NO 2 251.28 2 1 3 0 46.2 3 19 53317196 G19 C 14 H 8 ClNO 3 273.21 3 1 2.8 0 55.4 2 20 121217533 G20 C 11 H 11 NO 5 237.21 5 1 0.6 0 73.9 3 21 53957429 G21 C 8 H 4 BrNO 2 226.03 2 0 1.7 0 37.4 0 22 89617543 G22 C 8 H 5 NO 2 148.14 2 1 0.8 0 46.2 0 23 12963636374 G23 C 16 H 13 NO 2 204.22 3 0 0.7 0 40.6 1 24 129712608 G24 C 16 H 13 NO 2 260.32 3 0 2.3 0 40.6 4 25 129750791 G25 C 16 H 13 NO 2 249.29 4 0 1.4 0 71.9 2 26 AKO5015681801 G26 C 16 H 13 NO 2 275.3 4 0 2.2 0 63.7 4 27 129801452 G27 C 16 H 13 NO 2 219.2 5 1 0.2 0 78.8 3 28 129884125 G28 C 16 H 13 NO 2 188.14 4 0 1.8 0 51.7 1 29 129892573 G29 C 16 H 13 NO 2 173.12 3 0 0.4 0 54.5 0 30 57206086 G30 C 16 H 13 NO 2 190.2 3 1 0.8 0 49.4 1 31 SCHEMBL3013723 G31 C 16 H 13 NO 2 257.33 2 0 3.7 0 37.4 3 32 129816102 G32 C 16 H 13 NO 2 163.13 3 1 0 0 55.7 0 33 45382149 G33 C 16 H 13 NO 2 179.15 3 1 1.3 0 46.2 0 34 70398213 G34 C 16 H 13 NO 2 161.16 2 1 0.5 0 46.2 0 35 129750825 G35 C 16 H 13 NO 2 263.27 5 0 1.2 0 81.1 0 36 129887753 G36 C 16 H 13 NO 2 266.25 4 1 2.2 0 80.5 1 37 135522218 G37 C 16 H 13 NO 2 160.17 2 1 1 0 41.5 0 38 135705565 G38 C 11 H 12 N 2 O 188.23 2 1 1.7 0 41.5 1 39 135618153 G39 C 11 H 12 NO 2 188.23 2 1 1.8 0 41.5 2 40 71304019 G40 C 8 H 13 BrFNO 2 244.02 3 1 1.5 0 46.2 0 41 47002244 G41 C 10 H 8 N 4 O 2 216.20 4 0 1.5 0 51.7 3 42 54239611 G42 C 8 H 5 NO 5 S 227.20 5 1 0.2 0 100 1 43 135411931 G43 C 14 H 9 ClN 2 O 256.68 2 1 3.3 0 41.5 1 44 45382199 G44 C 9 H 6 FNO 2 179.15 3 1 1.2 0 46.5 0 45 54137095 G45 C 9 H 6 N 2 O 3 190.16 3 1 0.3 0 80.5 0 46 169447382 G46 C 9 H 4 F 3 NO 2 217.14 5 1 1.6 0 46.2 0 47 45382200 G47 C 9 H 5 BrFNO 2 258.04 3 1 1.9 0 46.2 0 48 135452553 G48 C 15 H 10 ClN3O 283.71 3 1 3.3 0 53.8 2 2.2 Target Identification Tumour necrosis factor-alpha's (TNF-α) three-dimensional X-ray crystallographic structure was acquired in PDB format from the Protein Data Bank (PDB) ( www.rcsb.org ). Pro-inflammatory cytokine TNF-α (PDB ID: 2AZ5), plays a crucial role in immune responses and the regulation of infections. 30 , 31 The following are the protein's structural specifics: Molecular Weight : 66.74 kDa Total Atom Count : 4,500 Modeled Residue Count : 542 Deposited Residue Count : 592 Protein Chains : A, B, C & D For molecular docking studies, all four chains of the TNF-α receptor (A, B, C, and D) were used. The protein was using MGL Tools , where non-essential components such as co-crystallized ligands, additional chains, and water molecules were removed. Furthermore, polar hydrogen atoms were added to enhance the docking accuracy. The identification of active sites within the protein-ligand complex is a fundamental step in drug discovery. To predict potential binding pockets, CavityPlus , an online computational tool, was used. The analysis revealed the presence of two binding pockets within the receptor. Among them, one binding site was selected based on druggability and surface area parameters to ensure optimal interaction with potential ligands 32 , 33 . 2.3 Docking study: Swiss dock (Autodock Vina) was used to conduct docking study of specific isatin derivatives with the TNF-α receptor. After that PDB files of the receptors and ligands were converted into PDBQT form. With the help of free online program CASTp 3.0, the binding site amino acid information was obtained, and the grid dimensions of the TNF-α receptor were established. Protein's grid size parameters are x=-13.871, y = 71.434, z = 26.906, and its default exhaustiveness is set to 8. The grid centre dimension was x = 30, y = 30, z = 30. Next, we used Swiss Dock (Autodock Vina) 34 to dock a few chosen isatin variants. The receptor was maintained as a hard target, but the ligands were flexible, possessing various rotatable bonds. The most stable ligands for the receptor were those with the highest binding affinities (more negative values) 35 . Every ligand binds to the receptor with a high affinity 36 – 38 . Later, we chose six ligands with binding affinities more than − 8.0 kcal/mol. The interactions of the nine chosen ligands with a binding afinity of more than − 8.0 kcal/mol were assessed using the free Biovia Discovery Studio 2024 client (Biovia 2020). Additionally, we created nine docking copies of six hit ligands and then took the highest binding energy score 39 – 41 in order to make our results more repeatable. The replica results from Table 2 are shown in supplementary material. 2.4 ADMET study The SWISS ADME and PKCSM free online web servers were used to verify the ADMET (Absorption, Digestion, Metabolism, Excretion, and Toxicity) 42 – 44 calculations of the top six isatin derivatives. The highest binding affinity for the TNF-α receptor was demonstrated by these top six ligands. Strong data is available on these servers to verify high-precision physiochemical characteristics like pharmacokinetics, toxicity, lipophilicity, water solubility, and drug-likeness 45 . Moreover, Lipinski's rule of five is not broken by any of the ligands. This demonstrates the oral bioavailability of every ligand. A total of six ligands demonstrated bioavailability and actively crossed the blood brain barrier (BBB) 46 . 2.5 Molecular Dynamics Simulation: A molecular dynamics (MD) simulation method based on an elastic network model is used by the iMODS (internal coordinates normal mode analysis) tool ( https://imods.iqf.csic.es/ ) and CABS flex 2.0 ( https://biocomp.chem.uw.edu.pl/CABSflex2 ) to investigate the mobility and structure flexibility of macromolecules respectively 47 , 48 . In contrast to traditional all-atom MD simulations, iMODS uses a coarse-grained approach that offers insights into molecular motion while drastically lowering computational cost. In order to depict collective movements in massive macromolecules like proteins and nucleic acids, the approach uses internal coordinates, such as torsion angles, to calculate a structure's normal modes 49 . According to this method, low-frequency normal modes match movements that are relevant to biology. Energy minimization and elastic network models are used in iMODS to approximate inter atomic interactions as harmonic springs, simulating the dynamic behavior of molecules. In order to build the elastic network, the material input contains a protein structure in PDB format that is preprocessed. The results of further computations include predictions for the B-factor, mobility profiles, and deformation trajectories 50 . Molecular flexibility and conformational changes may be investigated using this technique without the high resource requirements of full-scale MD simulations. By modeling the potential movements a structure might experience while retaining its integrity, it is especially useful in functional annotation, drug design, and comprehending protein processes 51 . 3. Result and Discussion 3.1 Retrieval of ligands and receptor The PubChem database is accessible online without charge. This database was utilised to prepare the legand dataset for this investigation. Without any filtering, approx 600 Isatin derivatives were chosen from the PubChem database. A total of 48 Isatin derivatives (Table 1 ) were produced between 2010 and 2023 by using the Lipinski rule of five for isatin derivatives. Receptors are available online through the PDB database. The target receptor for anti-encephalitis screening in this investigation is TNF-α. List of identified compounds having highest binding affinity (Fig. 1 , 2, 3 , 4, 5, 6 & 7 ) are as given below- 3.2 Molecular Docking interaction of ligand-receptor complexes: Information on the ligand–protein binding interaction process is vital in drug design since it can lead to developing new therapeutic candidates. Because it can offer crucial insights into drug development, design, and discovery, a fundamental grasp of the fundamentals of molecular interaction is therefore essential. An extensive in silico method for forecasting the binding mode of a ligand-protein interaction is molecular docking 34 , 35 . Every one of the 48 isatin-based ligands (Table 1 ) was successfully docked against the encephalitis disease's TNF-α receptor. The binding affinities between the ligand and the receptor ranged from − 4.49 kcal/mol to -9.20 kcal/mol. Table 2 Binding affinity and molecular interaction of selected candidates mentioned in the Fig. 8 – 14 . S.No. Figure Compound code SMILE file Highest Binding affinity (kcal/mole) Lowest Binding affinity (kcal/mole) Molecular interaction 1 Figure 8 G3 O = C1NC2 = C(C = C(CCC3 = CC = CC = C3)C = C2)C1 = O -8.3 -6.6 TYR(C:151), TYR(C:119), TYR(D:59) 2 Figure 9 G5 ClC1 = CC = CC(= C1)\C = C\C2 = CC3 = C(NC(= O)C3 = O)C = C2 -9.2 -7.8 GLN (B:125),ARG (B:82) 3 Figure 10 G15 ClC1 = CC = C(C = C1)C = N\N = C2/C(= O)NC3 = CC = CC = C23 -8.3 -7.1 TYR(D:59), TYR (C:119) 4 Figure 11 G17 FC1 = CC = CC(= C1)\C = C\C2 = CC3 = C(NC(= O)C3 = O)C = C2 -9.0 -8.0 TYR(C:59), TYR(D:59),LEU(C:120) 5 Figure 12 G18 O = C1NC2 = C(C = CC(= C2)CCC3 = CC = CC = C3)C1 = O -8.2 -7.1 TYR(C:119),TYR(C:151), TYR(D:59) 6 Figure 13 G48 ClC1 = CC = CC = C1\C = N\N = C2/C(= O)NC3 = CC = CC = C23 -8.6 -7.6 LEU(C:57), TYR(C:59), TYR(D:151), LEU(D:120),SER(D:60) 7 Figure 14 ACY C1 = NC2 = C(N1COCCO)N = C(NC2 = O)N -6.5 -5.9 SER(D:60), LEU(D:120) Furthermore, we selected six ligands with a binding affinity higher than − 8.0 kcal/mol, which is typically utilised as a standard in a number of docking studies. In order to make our investigation more thorough by this study. The standard medication was acyclovir. According to these in silico studies, compound G5 exhibited the highest binding score (-9.20 kcal/mol) through interactions with GLN B:125 & ARG B:82. Compound G17 came in second with a binding score of -9.00 kcal/mol through interactions with TYR C:59, TYR D:59 & LEU C:120, and compound G48 with a binding score of -8.60 kcal/mol through interactions with LEU C:57, TYR C:59, TYR D:151, LEU D:120 & SER D:60 with reference to Acyclovir (-6.50 kal/mol) through interactions with SER D:60 & LEU D:120. Table 2 lists these six ligands along with their chemical ID, docking interaction, and greatest and lowest binding affinities. The best compounds with a docking score of at least − 8.0 kcal/mol were chosen for ADMET analysis because the fundamental structural components of all the chosen isatin derivatives are comparable. 3.3 ADMET study: Important information about the pharmacokinetic characteristics and binding affinities of the selected drugs against the target protein linked to encephalitis is provided by the SwissADME and molecular docking study. The majority of the nominated compounds met favourable drug-likeness criteria, including Lipinski's Rule of Five compliance, which indicates strong oral bioavailability, according to SwissADME data. Additionally, the compounds showed favourable pharmacokinetic characteristics, such as minimal potential for blood-brain barrier (BBB) penetration and high gastrointestinal absorption. These characteristics are especially important for encephalitis treatments that must reduce central nervous system (CNS) toxicity. Table 3 Pharmacokinetic (ADMET) study of top six ligand molecules- S.No. Comp. ID Compound code Molecular formula Molecular weight(g/mol) HBA HBD Log P Value Violation TPSA (A 0 2 ) 1 53319928 G3 C 16 H 13 NO 2 251.28 2 1 3 0 46.2 2 44575103 G5 C 16 H 10 ClNO 2 283.71 2 1 3.7 0 46.2 3 135487314 G15 C 15 H 10 ClN 3 O 283.71 3 1 3.3 0 53.8 4 44575104 G17 C 16 H 10 FNO 2 267.25 3 1 3.2 0 46.2 5 53322432 G18 C 16 H 13 NO 2 251.28 2 1 3 0 46.2 6 135452553 G48 C 15 H 10 ClN3O 283.71 3 1 3.3 0 53.8 A potent graphical tool for forecasting pharmacokinetic characteristics, the SWISS ADME boiled egg model focuses on blood-brain barrier (BBB) permeability and gastrointestinal (GI) absorption. This model classifies a compound's possible behavior according to its physicochemical characteristics, providing a simple and intuitive method of assessing a compound's drug-likeness. Lipophilicity (WLOGP) and topological polar surface area (TPSA), two important factors that determine a compound's capacity to penetrate biological membranes, are represented by the x- and y-axes of a two-dimensional figure, respectively. Lipophilic (WLOGP) order of best docked compounds are G5 > G17 > G48 > G15 > G3 > G18 > ACY (standard) ( Fig. 15 ). To illustrate the absorption and permeability properties of substances, the model separates the plot into discrete areas, much like an egg. The yellow area, sometimes referred to as the "egg yolk," is made up of substances that are likely to pass through the blood-brain barrier because of their advantageous ratio of polar surface area to lipophilicity. These substances are potential targets for the central nervous system (CNS) because they are frequently nonpolar and have TPSA values low enough to allow passive diffusion across the lipid-rich environment of the BBB. Conversely, the white area around the yolk, known as the "egg white," denotes substances that have a high GI absorption rate but little ability to cross the blood-brain barrier. These substances usually have a modest polarity and can circulate throughout the body without affecting the central nervous system. It is anticipated that compounds that fall outside of the egg will have less than ideal pharmacokinetic characteristics. Individuals with low lipophilicity or a very high TPSA may have trouble with membrane permeability, which could lead to inadequate distribution and absorption. On the other hand, substances with exceptionally high lipophilicity can experience problems like nonspecific protein binding or metabolic instability, which would further impede their potential for therapeutic development. Individual compounds are represented by colored dots in the boiled egg model, and their plot location indicates the expected behavior of each component. Red spots in the white shell indicate good GI absorption without CNS penetration, whereas red dots in the yellow yolk indicate BBB permeability, which implies CNS activity. It is anticipated that compounds that fall outside of the egg will have less than ideal pharmacokinetic characteristics. Individuals with low lipophilicity or a very high TPSA may have trouble with membrane permeability, which could lead to inadequate distribution and absorption. On the other hand, substances with exceptionally high lipophilicity can experience problems like nonspecific protein binding or metabolic instability, which would further impede their potential for therapeutic development. Individual compounds are represented by colored dots in the boiled egg model, and their plot location indicates the expected behavior of each component. Red spots in the white shell indicate good GI absorption without CNS penetration, whereas red dots in the yellow yolk indicate BBB permeability, which implies CNS activity. Table 4 Metabolic and Toxicity study of top six ligand molecules- S.No. Compound code BBB penetration GI Absorption Skin pemeation(cm/s) WlogP Lipophilicity Metabolism Toxicity profile Pgp substrate CYP450 3A4 CYP450 1A2 CYP450 2C9 CYP450 2D6 Acute oral toxicity AMES(Mutagenicity) Carcinogenicity 1 G1 Yes High -6.25 2.04 Yes Yes No No III No No No 2 G2 Yes High -5.38 2.86 No Yes Yes No III Yes No No 3 G3 Yes High -5.72 2.54 No Yes Yes No III No No No 4 G4 Yes High -5.66 2.76 No Yes No No III Yes No No 5 G5 Yes High -5.67 2.04 No Yes No Yes III No No No 6 G6 Yes High -5.72 2.54 No Yes Yes No III No No No - ACY No High -8.78 -1.48 No No No No III Yes No No WLogP (lipophilic value) was higher for compound G5 (2.86), followed by compounds G17 (2.76), G48 (2.54), G15 (2.54), G18 (2.04), and G3 (2.04). Every compound complied with the Lipinski rule of five, and there was not a single exception mentioned in the Table 3 . The model helps find promising compounds and highlights potential obstacles by visualizing permeability and absorption properties. A computer method called SwissADME ( https://www.swissadme.ch/ ) & admetSAR software ( https://lmmd.ecust.edu.cn/admetsar2 ) were created to forecast the pharmacokinetic and toxicity profiles of small compounds, as well as their possible toxicological characteristics and interactions with cytochrome P450 enzymes (CYPs). The tool provides information on whether a substance may inhibit important CYP isoforms that are essential for drug metabolism and can affect metabolic stability and medication-drug interactions, including CYP3A4, CYP1A2, CYP2C9, and CYP2D6 described in Table 4 . Significant changes in pharmacokinetics may result from inhibition of certain CYP enzymes. For example, CYP3A4 is in charge of metabolising xenobiotics, including medications, and its inhibition may raise the plasma concentrations of medications taken together, which may have negative consequences. The clearance of these substances may also be impacted by the suppression of CYP1A2, a hepatic CYP protein that metabolises more than 100 substrates. Both CYP2C9 and CYP2D6 are important players in drug metabolism; CYP2D6 metabolises a large number of clinically prescribed pharmaceuticals, while CYP2C9 helps remove compounds with restricted therapeutic indices. The predictive models developed by SwissADME are based on large datasets and provide a binary (yes/no) prediction about a compound's ability to inhibit various CYP isoforms. 3.4 Molecular Dynamics Simulation study: To calculate, per-residue, and fluctuation in the peptide/protein complex, the protein structure (in PDB format with default parameters) was sent to CABS-flex software, which generates an output file with 10 modelled structures and the root mean-square fluctuation (RMSF) profile in a graph. The compound G3-receptor complex had the highest variation 3.4210 Å at residue number 22 and the lowest fluctuation 0.3220 Å at residue number 122. The compound G5-receptor complex had the highest variation 3.620 Å at residue number 146 and the lowest fluctuation 0.1270 Å at residue number 152. Similarly the compound G15-receptor complex had the highest variation 3.7560 Å at residue number 145 and the lowest fluctuation 0.0990 Å at residue number 28. The compound G17-receptor complex had the highest variation 5.4660 Å at residue number 70 and the lowest fluctuation 0.0970 Å at residue number 150. The compound G18-receptor complex had the highest variation 3.6020 Å at residue number 86 and the lowest fluctuation 0.0810 Å at residue number 120. The compound G48-receptor complex had the highest variation 2.7640 Å at residue number 22 and the lowest fluctuation 0.0680 Å at residue number 57. The compound ACY-receptor complex had the highest variation 3.6220 Å at residue number 146 and the lowest fluctuation 0.0790 Å at residue number 56. When utilizing iMODS software for molecular dynamics simulation, data analysis entails, interpreting a number of computational outputs that shed light on the flexibility and structural dynamics of biomolecules. Following the upload of a protein structure, iMODS uses an elastic network model to perform normal mode analysis (NMA), concentrating on low-frequency modes that depict large-scale, physiologically significant movements. Deformability plots, B-factor comparisons, eigenvalue spectra, variance maps, and covariance matrices are among the primary data outputs. Different ligand-receptor complexes displaying the amount of deformation that individual residues may undergo, the deformability plot highlights possible hinge areas and flexible loops [Figure 17 (A) & (B)] , therefore indicating the flexibility of each residue. In order to validate the projected movements, B-factor analysis compares experimental B-factors (which is often acquired from X-ray crystallography data with theoretical B-factors, which are derived from the normal modes). The energy needed for motion is measured using eigenvalue (6.422084e-05), which indicates the stiffness of each normal mode [Figure 17 (C)] . While the cumulative variance aid in determining the number of modes required capturing the majority of the molecular motion, the variance plot displays the mobility linked to each mode. By showing the associated motions between residue pairs, the covariance matrix focuses on domain relationships and collective behaviors [Figure 17 (D), (E) & (F)] . All of these investigations together provide a thorough understanding of the stability, flexibility, and possible functional movements of molecules. 4. Conclusion A game-changing strategy that greatly improves the effectiveness and accuracy of the drug discovery process is the use of computer-aided drug design (CADD) in the screening of anti-encephalitis candidates. This work found promising compounds with favorable pharmacokinetic profiles, high binding affinities, and drug-like properties by utilizing sophisticated computational approaches like molecular docking, ADME properties analysis t,. These results highlight how CADD can expedite lead compound generation, selection, optimization and validation, saving time and money compared to conventional approaches. Thus, substances that actively cross the blood-brain barrier and have a higher binding affinity with the receptor will be effective as anti-encephalitis medicines. All of the chosen compounds underwent molecular docking experiments, and we discovered that, in comparison to normal Acyclovir, compounds G5, G17, G48, G15, G3, and G18 demonstrated superior binding scores and lesser toxicity profile with better ADME against TNF-α, respectively. Along with molecular dynamics simulation and more advance tools, these results could open the door for more experimental research and preclinical assessments as well as clinical assessment, which will ultimately aid in the creation of more focused and efficient therapies for this crippling illness. Abbreviations BBB Blood brain barrier JEV Japanese Encephalitis Virus TNF-α Tumour Necrosis Factor alpha SDF Structure Data File TPSA Total polar Surface area ACY Acyclovir Declarations Ethics: NA Human & Animal Right: The research that forms the basis of this study did not involve the use of humans or animals. Publication Consent : Not relevant. Funding: No Clinical trial number: Not applicable Conflict of Interest: The writers disclose no financial or other conflicts of interest. Acknowledgement: I want to sincerely thank God for all of his blessings and direction during this journey. I also want to express my gratitude to my mentor, family, and friends for their constant inspiration, support, and encouragement. Author Contribution Pawan Kumar Gupta performed all the virtual lab work and wrote the main manuscript text file, and Shashi Bhooshan Tiwari reviewed and corrected the manuscript. References https://www.who.int/news-room/fact-sheets/detail/japanese-encephalitis. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 May, 2025 Reviews received at journal 27 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 14 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Vina\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6028232/v1/3b213008a9201fc2060417bc.jpg"},{"id":81252835,"identity":"44248245-d190-4c08-9100-3fd6418cf7a6","added_by":"auto","created_at":"2025-04-24 03:42:13","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":59000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eADME study of highest potential candidates (boiled egg model)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6028232/v1/0783c13ca48b29de7dac1000.jpg"},{"id":81252826,"identity":"0bdc3104-69bc-4ac2-9264-169719fab83f","added_by":"auto","created_at":"2025-04-24 03:42:13","extension":"jpg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":56761,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentation of RMSF of ligand-receptor complexes; (A)\u003c/strong\u003eRMSF of G3-receptor complex, \u003cstrong\u003e(B)\u003c/strong\u003eRMSF of G5-receptor complex,\u003cstrong\u003e (C)\u003c/strong\u003eRMSF of G15-receptor complex,\u003cstrong\u003e (D)\u003c/strong\u003eRMSF of G17-receptor complex,\u003cstrong\u003e (E)\u003c/strong\u003eRMSF of G18-receptor complex,\u003cstrong\u003e (F)\u003c/strong\u003eRMSF of G48-receptor complex,\u003cstrong\u003e (G)\u003c/strong\u003eRMSF of ACY-receptor complex\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6028232/v1/c97689ff9a0fa8bb5ee7ae29.jpg"},{"id":81253040,"identity":"4a0ee351-f1b3-4048-aa88-85fcc97dc021","added_by":"auto","created_at":"2025-04-24 03:50:12","extension":"jpg","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":84179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulation study of compound (A) Deformability region (B) B-factor comparisons (C) Eigen-value spectra (D) variance map (E) covariance matrix (F) Elastic network\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6028232/v1/9eef6cfd9388b9825afcf4f4.jpg"},{"id":81695973,"identity":"9facb55d-3b4b-4460-a9d8-c660e4c8db94","added_by":"auto","created_at":"2025-04-30 12:07:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2541740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6028232/v1/1935395c-0efd-42be-9d97-6ec129db6923.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIdentification and selection of potential TNF-α inhibitors as anti- encephalitis candidates by using in silico assisted drug design\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA serious neurological condition called encephalitis is characterized by inflammation of the brain\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and is frequently brought on by bacterial infections, viral infections, or autoimmune reactions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Because of potential for long-term neurological consequences, and rising incidence in certain regions \u0026ndash; particularly those where some areas where vector-borne encephalitis viruses like the Zika virus and Japanese encephalitis are endemic\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Currently, treatment options are limited to supportive care and broad-spectrum antivirals\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, which often fail to effectively target specific causative agents or prevent long-term complications\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These challenges highlight the urgent need for innovative and targeted therapeutic approaches.\u003c/p\u003e \u003cp\u003eCADD has emerged as a game changer in drug development, providing novel ways for effectively identifying and optimizing prospective therapeutic candidates\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Unlike traditional high-throughput screening methods, which are time and resource intensive\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, CADD employs computational techniques to predict drug-target interactions, screen large compound libraries, and refine lead molecules for improved efficacy and safety\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The integration of these approaches has proven important in the quick identification of inhibitors against critical viral proteins, host factors, and inflammatory pathways involved in encephalitis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The increasing use of artificial intelligence (AI) improves the predictive power of CADD, allowing researchers to predict complicated interactions\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, identify potential off-target effects, and optimize drug-like features with remarkable accuracy. These innovations speed up drug development, especially for urgent situations like developing encephalitis epidemics driven by arboviruses or new other infections \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Furthermore, the use of virtual screening and molecular dynamics simulations enables compound prioritization for experimental validation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, lowering the cost and time associated with laboratory-based drug development\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Recent investigations have successfully used CADD in the development\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e of anti-encephalitis drugs, discovering promising inhibitors of viral polymerases, proteases, and envelope proteins, as well as modulators of host immune responses. Such approaches highlight CADD's ability to address critical issues in encephalitis therapy\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, such as drug resistance and the necessity for pathogen-specific therapeutics. By merging computational tools with experimental procedures, CADD is a potent option for accelerating the development of innovative therapies to tackle this debilitating ailment\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study primarily focuses on Tumor Necrosis Factor-alpha (TNF-α), which is pro-inflammatory cytokine that plays a critical role in immune responses, cell survival, apoptosis, and inflammation. It binds to TNF receptors (TNFR1 and TNFR2), triggering intracellular signaling cascades like NF-κB and MAPK pathways, and leading to cytokine production, immune cell recruitment and inflammation\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Excessive TNF-α activity contributes to chronic inflammatory diseases such as rheumatoid arthritis, psoriasis, and Crohn\u0026rsquo;s disease. Inhibiting TNF-α helps mitigate pathological inflammation, preventing tissue damage and autoimmunity. Developing TNF-α inhibitors is crucial for controlling these conditions while balancing immune responses to avoid compromising host defense against infections and malignancies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis research exclusively investigates isatin derivatives as primarily candidate. Isatin (1H-indole-2,3-dione) is a heterocyclic compound first discovered in 1841 as an oxidation product of indigo. Structurally, it consists of an indole core with keto functional groups at the second and third positions, forming a unique bicyclic system\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The presence of these functional groups enables isatin to act as a versatile scaffold in medicinal chemistry, facilitating interactions with biological targets. The structural diversity of isatin derivatives arises from modifications at the nitrogen atom or the aromatic ring, which significantly influences their pharmacological properties. Due to its ability to interact with various enzymes and receptors, isatin and its derivatives are extensively studied for their therapeutic potential. They exhibit a broad spectrum of biological activities, including antimicrobial, antiviral, anticancer, anticonvulsant, and anti-inflammatory effects\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Additionally, their antiviral potential has been explored against various viruses, including influenza and corona viruses, by targeting viral replication pathways\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The structural versatility and bioactivity of isatin makes it a valuable scaffold in drug discovery. Continued research into its derivatives may lead to the development of novel therapeutics with improved efficacy and selectivity against various diseases\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Dataset generation:\u003c/h2\u003e \u003cp\u003eTo construct a comprehensive dataset, ligand data groups were selected from the PubChem database based on their structural diversity. The dataset preparation process involved systematic retrieval, curation, and processing of chemical to support research objectives. Initially, suitable compounds are found using keyword searches, assay findings, or structural similarities to known ligands. Approximately 600 Isatin derivative chemicals were found in the PubChem database after conducting a literature search. From the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a free online collection of chemicals, 48 isatin derivatives with decreased lethal activity and molecular weights smaller than 500 dalton were chosen using the Lipinski rule of five and new filtering techniques. All chemical structures were downloaded in .sdf format. The Swiss Dock tool\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e was used in Autodock vina mode to get ligands ready for docking studies. The resulting chemical structures were transferred to .pdb file format at the conclusion of the ligand production process.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003elist of 48 isatin derivatives with physicochemical parameters-\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComp. ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMolecular formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular weight(g/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHBA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLog P Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eViolation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTPSA (A\u003csup\u003e0 2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNumber of Rotatable Bond\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53399318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e191.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61356413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e267.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53319928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e251.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44603420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e13\u003c/sub\u003eH\u003csub\u003e15\u003c/sub\u003eBrN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e311.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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\u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129750961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e11\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eBrNO\u003csub\u003e2\u003c/sub\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e298.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129840580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e11\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e211.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53996225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e6\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129750642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e11\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e219.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1390519551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e7\u003c/sub\u003eBr\u003csub\u003e2\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e381.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.8\u003c/p\u003e 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\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70257034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eBrN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e92\u003c/p\u003e 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colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129710558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e11\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eNO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e237.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44575104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eFNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e267.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53322432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e251.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53317196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eClNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e273.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121217533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e11\u003c/sub\u003eH\u003csub\u003e11\u003c/sub\u003eNO\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e237.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53957429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003eBrNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e226.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89617543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e148.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12963636374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e204.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129712608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e260.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e 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colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129801452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e219.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129884125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e188.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129892573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57206086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCHEMBL3013723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e257.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129816102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e163.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45382149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e179.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70398213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e161.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129750825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG35\u003c/p\u003e \u003c/td\u003e 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align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135522218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003eG40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eBrFNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e244.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47002244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e216.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54239611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eNO\u003csub\u003e5\u003c/sub\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e227.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135411931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eClN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e256.68\u003c/p\u003e \u003c/td\u003e 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colname=\"c2\"\u003e \u003cp\u003e54137095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e6\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e190.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169447382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003e F\u003csub\u003e3\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e217.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45382200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e9\u003c/sub\u003eH\u003csub\u003e5\u003c/sub\u003eBrFNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e258.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135452553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eClN3O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e283.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Target Identification\u003c/h2\u003e \u003cp\u003eTumour necrosis factor-alpha's (TNF-α) three-dimensional X-ray crystallographic structure was acquired in PDB format from the Protein Data Bank (PDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://pubchem.ncbi.nlm.nih.gov\" target=\"_blank\"\u003ewww.rcsb.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pro-inflammatory cytokine TNF-α (PDB ID: 2AZ5), plays a crucial role in immune responses and the regulation of infections.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe following are the protein's structural specifics:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMolecular Weight\u003c/b\u003e: 66.74 kDa\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTotal Atom Count\u003c/b\u003e: 4,500\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eModeled Residue Count\u003c/b\u003e: 542\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDeposited Residue Count\u003c/b\u003e: 592\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProtein Chains\u003c/b\u003e: A, B, C \u0026amp; D\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFor molecular docking studies, all four chains of the TNF-α receptor (A, B, C, and D) were used. The protein was using \u003cb\u003eMGL Tools\u003c/b\u003e, where non-essential components such as co-crystallized ligands, additional chains, and water molecules were removed. Furthermore, polar hydrogen atoms were added to enhance the docking accuracy. The identification of active sites within the protein-ligand complex is a fundamental step in drug discovery. To predict potential binding pockets, \u003cb\u003eCavityPlus\u003c/b\u003e, an online computational tool, was used. The analysis revealed the presence of two binding pockets within the receptor. Among them, one binding site was selected based on \u003cb\u003edruggability\u003c/b\u003e and surface area parameters to ensure optimal interaction with potential ligands\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Docking study:\u003c/h2\u003e \u003cp\u003eSwiss dock (Autodock Vina) was used to conduct docking study of specific isatin derivatives with the TNF-α receptor. After that PDB files of the receptors and ligands were converted into PDBQT form. With the help of free online program CASTp 3.0, the binding site amino acid information was obtained, and the grid dimensions of the TNF-α receptor were established. Protein's grid size parameters are x=-13.871, y\u0026thinsp;=\u0026thinsp;71.434, z\u0026thinsp;=\u0026thinsp;26.906, and its default exhaustiveness is set to 8. The grid centre dimension was x\u0026thinsp;=\u0026thinsp;30, y\u0026thinsp;=\u0026thinsp;30, z\u0026thinsp;=\u0026thinsp;30. Next, we used Swiss Dock (Autodock Vina)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e to dock a few chosen isatin variants. The receptor was maintained as a hard target, but the ligands were flexible, possessing various rotatable bonds. The most stable ligands for the receptor were those with the highest binding affinities (more negative values)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Every ligand binds to the receptor with a high affinity\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Later, we chose six ligands with binding affinities more than \u0026minus;\u0026thinsp;8.0 kcal/mol. The interactions of the nine chosen ligands with a binding afinity of more than \u0026minus;\u0026thinsp;8.0 kcal/mol were assessed using the free Biovia Discovery Studio 2024 client (Biovia 2020). Additionally, we created nine docking copies of six hit ligands and then took the highest binding energy score\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e in order to make our results more repeatable. The replica results from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are shown in supplementary material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 ADMET study\u003c/h2\u003e \u003cp\u003eThe SWISS ADME and PKCSM free online web servers were used to verify the ADMET (Absorption, Digestion, Metabolism, Excretion, and Toxicity)\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e calculations of the top six isatin derivatives. The highest binding affinity for the TNF-α receptor was demonstrated by these top six ligands. Strong data is available on these servers to verify high-precision physiochemical characteristics like pharmacokinetics, toxicity, lipophilicity, water solubility, and drug-likeness\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Moreover, Lipinski's rule of five is not broken by any of the ligands. This demonstrates the oral bioavailability of every ligand. A total of six ligands demonstrated bioavailability and actively crossed the blood brain barrier (BBB)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Molecular Dynamics Simulation:\u003c/h2\u003e \u003cp\u003eA molecular dynamics (MD) simulation method based on an elastic network model is used by the iMODS (internal coordinates normal mode analysis) tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imods.iqf.csic.es/\u003c/span\u003e\u003cspan address=\"https://imods.iqf.csic.es/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CABS flex 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biocomp.chem.uw.edu.pl/CABSflex2\u003c/span\u003e\u003cspan address=\"https://biocomp.chem.uw.edu.pl/CABSflex2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to investigate the mobility and structure flexibility of macromolecules respectively\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In contrast to traditional all-atom MD simulations, iMODS uses a coarse-grained approach that offers insights into molecular motion while drastically lowering computational cost. In order to depict collective movements in massive macromolecules like proteins and nucleic acids, the approach uses internal coordinates, such as torsion angles, to calculate a structure's normal modes\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. According to this method, low-frequency normal modes match movements that are relevant to biology. Energy minimization and elastic network models are used in iMODS to approximate inter atomic interactions as harmonic springs, simulating the dynamic behavior of molecules. In order to build the elastic network, the material input contains a protein structure in PDB format that is preprocessed. The results of further computations include predictions for the B-factor, mobility profiles, and deformation trajectories\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Molecular flexibility and conformational changes may be investigated using this technique without the high resource requirements of full-scale MD simulations. By modeling the potential movements a structure might experience while retaining its integrity, it is especially useful in functional annotation, drug design, and comprehending protein processes\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Retrieval of ligands and receptor\u003c/h2\u003e \u003cp\u003eThe PubChem database is accessible online without charge. This database was utilised to prepare the legand dataset for this investigation. Without any filtering, approx 600 Isatin derivatives were chosen from the PubChem database. A total of 48 Isatin derivatives (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were produced between 2010 and 2023 by using the Lipinski rule of five for isatin derivatives. Receptors are available online through the PDB database. The target receptor for anti-encephalitis screening in this investigation is TNF-α. List of identified compounds having highest binding affinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 2, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 4, 5, 6 \u0026amp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e7\u003c/span\u003e) are as given below-\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Molecular Docking interaction of ligand-receptor complexes:\u003c/h2\u003e \u003cp\u003eInformation on the ligand\u0026ndash;protein binding interaction process is vital in drug design since it can lead to developing new therapeutic candidates. Because it can offer crucial insights into drug development, design, and discovery, a fundamental grasp of the fundamentals of molecular interaction is therefore essential. An extensive \u003cem\u003ein silico\u003c/em\u003e method for forecasting the binding mode of a ligand-protein interaction is molecular docking\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Every one of the 48 isatin-based ligands (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was successfully docked against the encephalitis disease's TNF-α receptor. The binding affinities between the ligand and the receptor ranged from \u0026minus;\u0026thinsp;4.49 kcal/mol to -9.20 kcal/mol.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinding affinity and molecular interaction of selected candidates mentioned in the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMILE file\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHighest Binding affinity (kcal/mole)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLowest Binding affinity (kcal/mole)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMolecular interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO\u0026thinsp;=\u0026thinsp;C1NC2\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;C(CCC3\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C3)C\u0026thinsp;=\u0026thinsp;C2)C1\u0026thinsp;=\u0026thinsp;O\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTYR(C:151), TYR(C:119), TYR(D:59)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC(=\u0026thinsp;C1)\\C\u0026thinsp;=\u0026thinsp;C\\C2\u0026thinsp;=\u0026thinsp;CC3\u0026thinsp;=\u0026thinsp;C(NC(=\u0026thinsp;O)C3\u0026thinsp;=\u0026thinsp;O)C\u0026thinsp;=\u0026thinsp;C2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGLN (B:125),ARG (B:82)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eG15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eClC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;C1)C\u0026thinsp;=\u0026thinsp;N\\N\u0026thinsp;=\u0026thinsp;C2/C(=\u0026thinsp;O)NC3\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-8.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-7.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTYR(D:59), TYR (C:119)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e11\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eG17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC(=\u0026thinsp;C1)\\C\u0026thinsp;=\u0026thinsp;C\\C2\u0026thinsp;=\u0026thinsp;CC3\u0026thinsp;=\u0026thinsp;C(NC(=\u0026thinsp;O)C3\u0026thinsp;=\u0026thinsp;O)C\u0026thinsp;=\u0026thinsp;C2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-9.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-8.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTYR(C:59), TYR(D:59),LEU(C:120)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e12\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eG18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eO\u0026thinsp;=\u0026thinsp;C1NC2\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;CC(=\u0026thinsp;C2)CCC3\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C3)C1\u0026thinsp;=\u0026thinsp;O\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-8.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-7.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eTYR(C:119),TYR(C:151), TYR(D:59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e13\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eG48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eClC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C1\\C\u0026thinsp;=\u0026thinsp;N\\N\u0026thinsp;=\u0026thinsp;C2/C(=\u0026thinsp;O)NC3\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-8.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-7.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eLEU(C:57), TYR(C:59), TYR(D:151), LEU(D:120),SER(D:60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e14\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eACY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC1\u0026thinsp;=\u0026thinsp;NC2\u0026thinsp;=\u0026thinsp;C(N1COCCO)N\u0026thinsp;=\u0026thinsp;C(NC2\u0026thinsp;=\u0026thinsp;O)N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-6.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-5.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSER(D:60), LEU(D:120)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we selected six ligands with a binding affinity higher than \u0026minus;\u0026thinsp;8.0 kcal/mol, which is typically utilised as a standard in a number of docking studies. In order to make our investigation more thorough by this study. The standard medication was acyclovir. According to these \u003cem\u003ein silico\u003c/em\u003e studies, compound G5 exhibited the highest binding score (-9.20 kcal/mol) through interactions with GLN B:125 \u0026amp; ARG B:82. Compound G17 came in second with a binding score of -9.00 kcal/mol through interactions with TYR C:59, TYR D:59 \u0026amp; LEU C:120, and compound G48 with a binding score of -8.60 kcal/mol through interactions with LEU C:57, TYR C:59, TYR D:151, LEU D:120 \u0026amp; SER D:60 with reference to Acyclovir (-6.50 kal/mol) through interactions with SER D:60 \u0026amp; LEU D:120. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists these six ligands along with their chemical ID, docking interaction, and greatest and lowest binding affinities. The best compounds with a docking score of at least \u0026minus;\u0026thinsp;8.0 kcal/mol were chosen for ADMET analysis because the fundamental structural components of all the chosen isatin derivatives are comparable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 ADMET study:\u003c/h2\u003e \u003cp\u003eImportant information about the pharmacokinetic characteristics and binding affinities of the selected drugs against the target protein linked to encephalitis is provided by the SwissADME and molecular docking study. The majority of the nominated compounds met favourable drug-likeness criteria, including Lipinski's Rule of Five compliance, which indicates strong oral bioavailability, according to SwissADME data. Additionally, the compounds showed favourable pharmacokinetic characteristics, such as minimal potential for blood-brain barrier (BBB) penetration and high gastrointestinal absorption. These characteristics are especially important for encephalitis treatments that must reduce central nervous system (CNS) toxicity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePharmacokinetic (ADMET) study of top six ligand molecules-\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComp. ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompound code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMolecular formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMolecular weight(g/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHBA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLog P Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eViolation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTPSA (A\u003csup\u003e0 2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53319928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e251.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44575103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eClNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e283.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135487314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eClN\u003csub\u003e3\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e283.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44575104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eFNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e267.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53322432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e13\u003c/sub\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e251.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135452553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e10\u003c/sub\u003eClN3O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e283.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e53.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA potent graphical tool for forecasting pharmacokinetic characteristics, the SWISS ADME boiled egg model focuses on blood-brain barrier (BBB) permeability and gastrointestinal (GI) absorption. This model classifies a compound's possible behavior according to its physicochemical characteristics, providing a simple and intuitive method of assessing a compound's drug-likeness. Lipophilicity (WLOGP) and topological polar surface area (TPSA), two important factors that determine a compound's capacity to penetrate biological membranes, are represented by the x- and y-axes of a two-dimensional figure, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLipophilic (WLOGP) order of best docked compounds are \u003cb\u003eG5\u0026thinsp;\u0026gt;\u0026thinsp;G17\u0026thinsp;\u0026gt;\u0026thinsp;G48\u0026thinsp;\u0026gt;\u0026thinsp;G15\u0026thinsp;\u0026gt;\u0026thinsp;G3\u0026thinsp;\u0026gt;\u0026thinsp;G18\u0026thinsp;\u0026gt;\u0026thinsp;ACY (standard) (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e15\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e To illustrate the absorption and permeability properties of substances, the model separates the plot into discrete areas, much like an egg. The yellow area, sometimes referred to as the \"egg yolk,\" is made up of substances that are likely to pass through the blood-brain barrier because of their advantageous ratio of polar surface area to lipophilicity. These substances are potential targets for the central nervous system (CNS) because they are frequently nonpolar and have TPSA values low enough to allow passive diffusion across the lipid-rich environment of the BBB. Conversely, the white area around the yolk, known as the \"egg white,\" denotes substances that have a high GI absorption rate but little ability to cross the blood-brain barrier. These substances usually have a modest polarity and can circulate throughout the body without affecting the central nervous system. It is anticipated that compounds that fall outside of the egg will have less than ideal pharmacokinetic characteristics. Individuals with low lipophilicity or a very high TPSA may have trouble with membrane permeability, which could lead to inadequate distribution and absorption.\u003c/p\u003e \u003cp\u003eOn the other hand, substances with exceptionally high lipophilicity can experience problems like nonspecific protein binding or metabolic instability, which would further impede their potential for therapeutic development. Individual compounds are represented by colored dots in the boiled egg model, and their plot location indicates the expected behavior of each component. Red spots in the white shell indicate good GI absorption without CNS penetration, whereas red dots in the yellow yolk indicate BBB permeability, which implies CNS activity.\u003c/p\u003e \u003cp\u003eIt is anticipated that compounds that fall outside of the egg will have less than ideal pharmacokinetic characteristics. Individuals with low lipophilicity or a very high TPSA may have trouble with membrane permeability, which could lead to inadequate distribution and absorption. On the other hand, substances with exceptionally high lipophilicity can experience problems like nonspecific protein binding or metabolic instability, which would further impede their potential for therapeutic development. Individual compounds are represented by colored dots in the boiled egg model, and their plot location indicates the expected behavior of each component. Red spots in the white shell indicate good GI absorption without CNS penetration, whereas red dots in the yellow yolk indicate BBB permeability, which implies CNS activity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetabolic and Toxicity study of top six ligand molecules-\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompound code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBBB penetration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGI Absorption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSkin pemeation(cm/s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWlogP\u003c/p\u003e \u003cp\u003eLipophilicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eMetabolism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eToxicity profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePgp substrate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCYP450 3A4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCYP450 1A2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCYP450 2C9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCYP450 2D6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAcute oral toxicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAMES(Mutagenicity)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eCarcinogenicity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWLogP (lipophilic value) was higher for compound G5 (2.86), followed by compounds G17 (2.76), G48 (2.54), G15 (2.54), G18 (2.04), and G3 (2.04). Every compound complied with the Lipinski rule of five, and there was not a single exception mentioned in the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The model helps find promising compounds and highlights potential obstacles by visualizing permeability and absorption properties. A computer method called SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.swissadme.ch/\u003c/span\u003e\u003cspan address=\"https://www.swissadme.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u0026amp; admetSAR software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lmmd.ecust.edu.cn/admetsar2\u003c/span\u003e\u003cspan address=\"https://lmmd.ecust.edu.cn/admetsar2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were created to forecast the pharmacokinetic and toxicity profiles of small compounds, as well as their possible toxicological characteristics and interactions with cytochrome P450 enzymes (CYPs). The tool provides information on whether a substance may inhibit important CYP isoforms that are essential for drug metabolism and can affect metabolic stability and medication-drug interactions, including CYP3A4, CYP1A2, CYP2C9, and CYP2D6 described in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Significant changes in pharmacokinetics may result from inhibition of certain CYP enzymes. For example, CYP3A4 is in charge of metabolising xenobiotics, including medications, and its inhibition may raise the plasma concentrations of medications taken together, which may have negative consequences. The clearance of these substances may also be impacted by the suppression of CYP1A2, a hepatic CYP protein that metabolises more than 100 substrates. Both CYP2C9 and CYP2D6 are important players in drug metabolism; CYP2D6 metabolises a large number of clinically prescribed pharmaceuticals, while CYP2C9 helps remove compounds with restricted therapeutic indices. The predictive models developed by SwissADME are based on large datasets and provide a binary (yes/no) prediction about a compound's ability to inhibit various CYP isoforms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Molecular Dynamics Simulation study:\u003c/h2\u003e \u003cp\u003eTo calculate, per-residue, and fluctuation in the peptide/protein complex, the protein structure (in PDB format with default parameters) was sent to CABS-flex software, which generates an output file with 10 modelled structures and the root mean-square fluctuation (RMSF) profile in a graph.\u003c/p\u003e \u003cp\u003eThe compound G3-receptor complex had the highest variation 3.4210 \u0026Aring; at residue number 22 and the lowest fluctuation 0.3220 \u0026Aring; at residue number 122. The compound G5-receptor complex had the highest variation 3.620 \u0026Aring; at residue number 146 and the lowest fluctuation 0.1270 \u0026Aring; at residue number 152. Similarly the compound G15-receptor complex had the highest variation 3.7560 \u0026Aring; at residue number 145 and the lowest fluctuation 0.0990 \u0026Aring; at residue number 28. The compound G17-receptor complex had the highest variation 5.4660 \u0026Aring; at residue number 70 and the lowest fluctuation 0.0970 \u0026Aring; at residue number 150. The compound G18-receptor complex had the highest variation 3.6020 \u0026Aring; at residue number 86 and the lowest fluctuation 0.0810 \u0026Aring; at residue number 120. The compound G48-receptor complex had the highest variation 2.7640 \u0026Aring; at residue number 22 and the lowest fluctuation 0.0680 \u0026Aring; at residue number 57. The compound ACY-receptor complex had the highest variation 3.6220 \u0026Aring; at residue number 146 and the lowest fluctuation 0.0790 \u0026Aring; at residue number 56.\u003c/p\u003e \u003cp\u003eWhen utilizing iMODS software for molecular dynamics simulation, data analysis entails, interpreting a number of computational outputs that shed light on the flexibility and structural dynamics of biomolecules. Following the upload of a protein structure, iMODS uses an elastic network model to perform normal mode analysis (NMA), concentrating on low-frequency modes that depict large-scale, physiologically significant movements.\u003c/p\u003e \u003cp\u003eDeformability plots, B-factor comparisons, eigenvalue spectra, variance maps, and covariance matrices are among the primary data outputs. Different ligand-receptor complexes displaying the amount of deformation that individual residues may undergo, the deformability plot highlights possible hinge areas and flexible loops \u003cb\u003e[Figure 17 (A) \u0026amp; (B)]\u003c/b\u003e, therefore indicating the flexibility of each residue. In order to validate the projected movements, B-factor analysis compares experimental B-factors (which is often acquired from X-ray crystallography data with theoretical B-factors, which are derived from the normal modes). The energy needed for motion is measured using eigenvalue (6.422084e-05), which indicates the stiffness of each normal mode \u003cb\u003e[Figure 17 (C)]\u003c/b\u003e. While the cumulative variance aid in determining the number of modes required capturing the majority of the molecular motion, the variance plot displays the mobility linked to each mode. By showing the associated motions between residue pairs, the covariance matrix focuses on domain relationships and collective behaviors \u003cb\u003e[Figure 17 (D), (E) \u0026amp; (F)]\u003c/b\u003e. All of these investigations together provide a thorough understanding of the stability, flexibility, and possible functional movements of molecules.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eA game-changing strategy that greatly improves the effectiveness and accuracy of the drug discovery process is the use of computer-aided drug design (CADD) in the screening of anti-encephalitis candidates. This work found promising compounds with favorable pharmacokinetic profiles, high binding affinities, and drug-like properties by utilizing sophisticated computational approaches like molecular docking, ADME properties analysis t,. These results highlight how CADD can expedite lead compound generation, selection, optimization and validation, saving time and money compared to conventional approaches. Thus, substances that actively cross the blood-brain barrier and have a higher binding affinity with the receptor will be effective as anti-encephalitis medicines. All of the chosen compounds underwent molecular docking experiments, and we discovered that, in comparison to normal Acyclovir, compounds G5, G17, G48, G15, G3, and G18 demonstrated superior binding scores and lesser toxicity profile with better ADME against TNF-α, respectively. Along with molecular dynamics simulation and more advance tools, these results could open the door for more experimental research and preclinical assessments as well as clinical assessment, which will ultimately aid in the creation of more focused and efficient therapies for this crippling illness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBBB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood brain barrier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJapanese Encephalitis Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumour Necrosis Factor alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructure Data File\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal polar Surface area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcyclovir\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics: NA\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eHuman \u0026amp; Animal Right:\u003c/strong\u003e The research that forms the basis of this study did not involve the use of humans or animals.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003ePublication Consent\u003c/strong\u003e: Not relevant.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe writers disclose no financial or other conflicts of interest.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e I want to sincerely thank God for all of his blessings and direction during this journey. I also want to express my gratitude to my mentor, family, and friends for their constant inspiration, support, and encouragement.\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePawan Kumar Gupta performed all the virtual lab work and wrote the main manuscript text file, and Shashi Bhooshan Tiwari reviewed and corrected the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ehttps://www.who.int/news-room/fact-sheets/detail/japanese-encephalitis. \u003c/li\u003e\n\u003cli\u003eTran Minh Quan, Tran Thi Nhu Thao, Nguyen Manh Duy, Tran Minh Nhat, Hannah Clapham (2020) Estimates of the global burden of Japanese encephalitis and the impact of vaccination from 2000-2015 https://doi.org/10.7554/eLife.51027\u003c/li\u003e\n\u003cli\u003eHao Wang, Shaohua Zhao, Shengjun Wang, Yue Zheng, Shaohua Wang, Hui Chen, Jiaojiao Pang, Juan Ma, Xiaorong Yang, Yuguo Chen, Global magnitude of encephalitis burden and its evolving pattern over the past 30 years, Journal of Infection, Volume 84, Issue 6, 2022, Pages 777-787, ISSN 0163-4453, https://doi.org/10.1016/j.jinf.2022.04.026.\u003c/li\u003e\n\u003cli\u003eAli M Alam, Ava Easton, Timothy R Nicholson, Sarosh R Irani, Nicholas W S Davies, Tom Solomon, Benedict D Michael, Encephalitis: diagnosis, management and recent advances in the field of encephalitis, \u003cem\u003ePostgraduate Medical Journal\u003c/em\u003e, Volume 99, Issue 1174, August 2023, Pages 815\u0026ndash;825, https://doi.org/10.1136/postgradmedj-2022-141812\u003c/li\u003e\n\u003cli\u003eBoucher, A.; Herrmann, J. 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Natural Product Communications. 2021 Sep;16(9):1934578X211031707. https://doi.org/10.1177/1934578X211031707 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"TNF-α, molecular docking, anti-encephalitis, Isatin, CADD","lastPublishedDoi":"10.21203/rs.3.rs-6028232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6028232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEncephalitis, an inflammatory disorder of the brain caused by infections or autoimmune responses, is still a major global health concern due to its high morbidity, mortality, and long-term neurological consequences. The available therapy alternatives are usually confined, and conventional drug discovery procedures are both resource-intensive and time-consuming. These challenges underscore the critical need for novel tools to speed up therapy development, which could revolutionize treatment regimens and enhance results for impacted people globally.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo identify, optimize \u0026amp; selection of TNF-α inhibitors as anti-encephalitis candidates by using \u003cem\u003ein silico\u003c/em\u003e assisted drug design approach.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eComputer-aided drug design (CADD) is a revolutionize tool for identifying potential anti-encephalitis candidates through its ability to simulate molecular interactions, predict drug-target affinities, and optimize pharmacokinetic properties with molecular dynamics simulation. In this study, we have employed comprehensive \u003cem\u003ein silico\u003c/em\u003e method to find effective therapeutic compounds that targets the TNF-α receptor in encephalitis.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eAn \u003cem\u003ein silico\u003c/em\u003e screening of nearly 600 isatin derivatives were conducted by using data from the PubChem database. It was carried out using the Lipinski rule of five in addition to other criteria. After filtering, 48 Isatin derivatives were selected, and six compounds with binding affinities exceeding \u0026minus;\u0026thinsp;8.0 kcal/mol were identified as potential candidates. Additionally it was subjected to ADME analysis using Swiss ADME software. Every contender demonstrated greater binding affinity and actively crossed the BBB. Protein-ligand complexes were subjected to molecular dynamics (MD) simulations using CABS-flexV2.0 and the iMOD server in order to assess the root-mean-square fluctuations (RMSFs) and quantify protein stability respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHence, it is concluded that compounds with higher binding affinity and actively cross BBB values showed effective anti-encephalitis agents. We have performed molecular docking studies, ADMET analysis and MD simulation of all selected compounds and found that compounds G5, G17, G48, G15, G3 and G18 have showed better binding score against \u003cb\u003eTNF-α\u003c/b\u003e in contrast to standard drug Acyclovir.\u003c/p\u003e","manuscriptTitle":"Identification and selection of potential TNF-α inhibitors as anti- encephalitis candidates by using in silico assisted drug design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 03:42:07","doi":"10.21203/rs.3.rs-6028232/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-09T11:07:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T00:45:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329314384695751169650458952726465926272","date":"2025-04-24T08:28:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T19:18:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191050203521347652163610728205957203757","date":"2025-04-23T04:37:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T14:20:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11691137167019503129524613810896966902","date":"2025-04-22T12:06:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-22T07:12:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T10:29:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Chemistry","date":"2025-04-14T10:16:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43b14dd6-19da-4ec1-bb50-5d7aacc91018","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T15:08:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 03:42:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6028232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6028232","identity":"rs-6028232","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}