Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction.

ABSTRACT:

Motivation

The field of protein-ligand binding affinity prediction continues to face significant challenges. While deep learning (DL) models can leverage 3D structural information of protein-ligand complexes, they perform well only on heavily biased test sets containing information leaked from training sets. This lack of generalization arises from the limited availability of training data and the models' inability to effectively learn from protein-ligand interactions. Since these interactions are inherently time-dependent, molecular dynamics (MD) simulations offer a potential solution by incorporating conformational sampling and providing interaction rich information.

Results

We have developed MDbind, a dataset comprising 63 000 simulations of protein-ligand interactions, al

SUBMITTER: Libouban PY 

PROVIDER: S-EPMC12371333 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature