Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials

2024/01/29 by Francesc Sabanés Zariquiey, Raimondas Galvelis, Zariquiey, Francesc Sabanes +9 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Physical sciences #Monoclonal and Polyclonal Antibodies Research #Quantitative Methods (q-bio.QM) #vaccines and immunoinformatics approaches

paper · pdf · doi:10.48550/arxiv.2401.16062

openalex publication_date 2024/01/29 · openalex created_date 2024/01/31 · openalex updated_date 2026/07/28

Abstract

This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM). We compute relative binding free energies (RBFE) with the Alchemical Transfer Method (ATM) and validate its performance against established benchmarks and find significant enhancements compared to conventional MM force fields like GAFF2.

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