AUTHOR=Ahmadi Armin , Gupta Shivangi , Menon Vineetha , Baudry Jerome 

TITLE=Linking machine learning and biophysical structural features in drug discovery

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 11 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2024.1305272

DOI=10.3389/fmolb.2024.1305272

ISSN=2296-889X

ABSTRACT=IntroductionMachine 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.MethodsUsing 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.ResultsResults 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.ConclusionUnlike 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 ML and pharmacophoric analysis to develop intuitive and mechanism-driven tools for lead optimization and rational drug design.