Linking machine learning and biophysical structural features in drug discovery.

ABSTRACT:

Introduction

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.

Methods

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.

Results

Results showed signifi

SUBMITTER: Ahmadi A 

PROVIDER: S-EPMC11798802 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature