Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

ABSTRACT: The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sampling. Recent advances in machine learning have gained traction for protein-ligand binding affinity predictions in early drug discovery programs. In this article, we perform retrospective binding free energy evaluations for 172 compounds from our internal collection spread over four different protein targets and five congeneric ligand series. We compared multiple state-of-the-art free energy methods ranging from physics-based methods with different levels of complexity and conformational sampling to state-of-the-art machine-learning-based meth

SUBMITTER: Bansal N 

PROVIDER: S-EPMC10893267 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature