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

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 principal component analysis (PCA) and ensemble learning with a stacking regressor. During benchmarking, PLAIG achieved a PCC of 0.78 on 4852 complexes from the PDBbind v.2019 refined set and 0.82 on 285 complexes from the v.2016 core set, outperforming many existing models. External validation on the DUDE-Z data set demonstrated its ability to differentiate active ligands from decoys, achieving an average AUC of 0.69 and a maximum AUC of 0.89. To enrich de novo prediction capabilities for subsequent model versions, PLAIG was hybridized with sequence- and structure-based models. The hybrid models achieved an average PCC of 0.88 on well-known drug-target complexes, with the best reaching a PCC of 0.98. Future work will incorporate an explicit inclusion of a docking methodology into PLAIG's pipeline and assess its performance on de novo ligands. PLAIG is freely available at https://plaig-demo.streamlit.app/.