PLAIG: Protein-Ligand Binding Affinity Prediction Using a Novel Interaction-Based Graph Neural Network Framework.

ABSTRACT: Rapid prediction of protein-ligand binding affinity is important in the drug discovery process. The advent of machine learning methods has increased the speed of these predictions. Previous machine learning models based on structural, sequence, and interaction-based approaches have shown potential but often tend to memorize training data due to incomplete feature representations that lead to poor generalization on external complexes. To address this challenge, here, we developed PLAIG, a Graph Neural Network (GNN)-based machine learning framework for generalized binding affinity prediction. PLAIG represents binding complexes as graphs, integrating protein-ligand interactions and molecular topology to uniquely capture interaction and structural features. To reduce overfitting, we tested pri

SUBMITTER: Samudrala MV 

PROVIDER: S-EPMC12183606 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature