[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126113-en":3,"doc-seo-126113-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126113,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Linking machine learning and biophysical structural features in drug discovery - Original research","The study links machine learning with biophysical structural features to improve drug discovery by identifying pharmacophore characteristics tied to ligand-selected protein conformations. Molecular dynamics simulations generate an ensemble of protein binding-site conformations capturing binding-site dynamics, while pharmacophore descriptors drive an AI/ML prioritization framework for features unique to ligand-selected states. The method enriches true positive ligands by up to 54-fold versus random selection and is robust across diverse proteins. Unlike conventional screening, the work emphasizes conformational selection, making binding interactions interpretable and actionable for lead optimization.","TYPE Original Research PUBLISHED 23 January 2025  \nDOI 10.3389/fmolb.2024.1305272  \nOPEN ACCESS  \nEDITED BY  \nOgnjen Perisic,  \nBig Blue Genomics/Redesign Science, Serbia  \nREVIEWED BY  \nGiulia Morra,  \nNational Research Council (CNR), Italy Karan Kapoor,  \nEnsem Therapeutics, United States  \n*CORRESPONDENCE  \nJerome Baudry,  \n [jerome.baudry@uah.edu](jerome.baudry@uah.edu)[ ](jerome.baudry@uah.edu)Vineetha Menon,  \n [vineetha.menon@uah.edu](vineetha.menon@uah.edu)  \n†These authors have contributed equally to this work  \nRECEIVED 01 October 2023  \nACCEPTED 27 December 2024  \nPUBLISHED 23 January 2025  \nCITATION  \nAhmadi A, Gupta S, Menon V and Baudry J (2025) Linking machine learning and biophysical structural features in drug discovery.  \nFront. Mol. Biosci. 11:1305272 .  \ndoi: 10.3389/fmolb.2024.1305272  \nCOPYRIGHT  \n© 2025 Ahmadi, Gupta, Menon and Baudry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nLinking machine learning and biophysical structural features in drug discovery  \nArmin Ahmadi 1†, Shivangi Gupta 2†, Vineetha Menon 2* and Jerome Baudry 1*  \n1 Department of Biological Sciences, The University of Alabama in Huntsville, Huntsville, AL, United States, 2 Department of Computer Science, The University of Alabama in Huntsville, Huntsville, AL, United States  \nIntroduction: Machine learning methods were applied to analyze pharmacophore features derived from four protein-binding sites, aiming to identify key features associated with ligand-specific protein conformations.  \nMethods: Using molecular dynamics simulations, we generated an ensemble of protein conformations to capture the dynamic nature of their binding sites. By leveraging pharmacophore descriptors, the AI/ML framework prioritized features uniquely associated with ligand-selected conformations, enabling a mechanism-driven understanding of binding interactions. This novel approach integrates biophysical insights with machine learning, focusing on pharmacophoric properties such as charge, hydrogen bonding, hydrophobicity, and aromaticity.  \nResults: Results showed significant enrichment of true positive ligands—improving database enrichment by up to 54-fold compared to random selection—demonstrating the robustness of this approach across diverse proteins.  \nConclusion: Unlike conventional structure-based or ligand-based screening methods, this work emphasizes the role of specific protein conformations in driving ligand binding, making the process highly interpretable and actionable for drug discovery. The key innovation lies in identifying pharmacophore features tied to conformations selected by ligands, offering a predictive framework for optimizing drug candidates. This study illustrates the potential of combining MLand pharmacophoric analysis to develop intuitive and mechanism-driven tools for lead optimization and rational drug design.  \nKEYWORDS  \ndrug discovery, machine learning, pharmacophore, conformational selection, docking, ensemble docking, chemical biology  \n1 Introduction  \nContemporary approaches in computational drug discovery and computational chemical biology are mostly centered around protein: ligand predictions and rationalizations, in particular through using docking approaches. Docking is not only used to identify a specific binding mode for a particular ligand in a given protein target, but it is also used, and maybe mostly so nowadays, as a virtual screening tool that will “reduce the size of the chemical haystack” and allow a faster  \nFrontiers in Molecular Biosciences 01 [frontiersin.org](frontiersin.org)  \nand more economical identif","cbCaieBdwrha3ucV","https://ap.wps.com/l/cbCaieBdwrha3ucV","pdf",16162961,7,1,10,"English","en",105,"# Introduction\n## Conformational selection and ensemble docking\n## Machine learning for explainable feature identification\n# Methods\n## Molecular dynamics ensemble generation\n## Pharmacophore descriptors and AI/ML prioritization\n# Results\n## Database enrichment performance\n# Conclusion\n## Interpretable, mechanism-driven screening","[{\"question\":\"How does the approach generate protein conformational information for screening?\",\"answer\":\"It uses molecular dynamics simulations to generate an ensemble of protein binding-site conformations that reflect the dynamic nature of binding sites, which are then evaluated through docking and feature analysis.\"},{\"question\":\"What does the machine learning framework prioritize?\",\"answer\":\"The framework prioritizes pharmacophore features uniquely associated with ligand-selected protein conformations, using pharmacophore descriptors that reflect biophysical properties such as charge, hydrogen bonding, hydrophobicity, and aromaticity.\"},{\"question\":\"How much does the method improve enrichment compared with random selection?\",\"answer\":\"The results show significant enrichment of true positive ligands, improving database enrichment by up to 54-fold compared with random selection.\"}]","Linking machine learning and biophysical structural features in drug discovery - 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