From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

ABSTRACT: Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD

SUBMITTER: Min Y 

PROVIDER: S-EPMC11516055 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature