{"paper_id":"220dc208-5838-4d98-bf86-34d8a67c93f5","body_text":"In silico QSAR and design of chalcone derivatives for HT-29 colorectal cancer: MLR and ANN approaches | 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 In silico QSAR and design of chalcone derivatives for HT-29 colorectal cancer: MLR and ANN approaches Tony Nyo, Liling Triyasmono, Uripto Trisno Santoso This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9044642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, necessitating the continuous discovery of potent and selective therapeutic agents. Chalcone derivatives have demonstrated significant cytotoxic potential against the HT-29 colorectal cancer cell line. This study aimed to develop robust Quantitative Structure-Activity Relationship (QSAR) models to predict anticancer activity and design novel chalcone derivatives by comparing Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) approaches. A dataset comprising 193 chalcone derivatives was analyzed using 2D molecular descriptors. Model reliability was rigorously evaluated through internal validation (LOO and LMO) and external cross-validation (Q_F1^2, Q_F2^2, Q_F3^2). The results demonstrated that the Stepwise MLR model (27 descriptors) outperformed the ANN approach, exhibiting superior stability and predictive power with R2 = 0.817, Q_LOO^2= 0.744, and RMSEP = 0.217. In contrast, the ANN model (13i-8N-1O architecture) showed clear indications of overfitting with a negative Q_LMO^2 of -1.957. The most influential descriptors identified were QCmin (+1.173), MATSv2 (+1.043), and UI (-0.806). Based on the optimized model, a novel lead compound, Modifikasi_W_136a, was designed with chloro, fluoro, and trifluoromethoxy substitutions, achieving a predicted pIC50 of 7.04. An in silico toxicity assessment using ProTox-III revealed a Class 4 acute toxicity profile with favorable hepatotoxicity and genotoxicity predictions, though specific alerts for nephrotoxicity and cardiotoxicity were identified requiring experimental follow-up. This study provides a validated computational framework for the rational design of colorectal anticancer agents with integrated safety profiling. Medicinal Chemistry Computational Chemistry MLR ANN Colorectal Cancer Chalcone Toxicity Full Text Additional Declarations The authors declare no competing interests. Supplementary Files TonyNyosArticleSupplementaryInformation.docx Supplementary Information In silico QSAR and design of chalcone derivatives for HT-29 colorectal cancer: MLR and ANN approaches Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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