GAMT-GINE: A Graph Isomorphism Network Integrating Continuous Spatial Awareness and Multi-Task Learning for Protein–Ligand Binding Affinity Prediction

Abstract

Protein–ligand interactions (PLIs) play a crucial role in drug discovery, and accurately predicting protein–ligand binding affinity (PLA) remains a central challenge in computer-aided drug design. Although graph neural networks (GNNs) have demonstrated considerable potential in molecular modeling, existing methods still face several limitations, including excessive reliance on hand-crafted chemical features, loss of spatial information, and difficulties in integrating heterogeneous affinity labels, which restrict their generalization capability in PLA prediction. To address these challenges, we propose GAMT-GINE, a graph isomorphism network that integrates continuous spatial awareness with multi-task learning. The model employs minimalist atomic features and a batch-normalization-free mechanism, together with a multi-task branch that uses a large amount of half-maximal inhibitory concentration (IC50) data as an auxiliary prediction target. Experimental results show that GAMT-GINE achieves a Pearson’s correlation coefficient (Rp) of 0.791 and a root mean square error (RMSE) of 1.403 on the CASF-2013 benchmark dataset. In the generalization evaluation on CASF-2016, Rp further increases to 0.831, while RMSE decreases to 1.227, demonstrating performance comparable to that of current State-of-the-Art models. Furthermore, comprehensive evaluations, including ablation studies, feature importance analysis, analysis of the effects of data filtering on model performance and data composition, and analysis of the influence of training–test data similarity on prediction results, indicate that GAMT-GINE can effectively utilize continuous spatial information and heterogeneous affinity labels, achieving good predictive accuracy and cross-dataset generalization capability.