Structure-Aware Heterogeneous Information Fusion Framework for Protein–Ligand Binding Affinity Prediction

Accurate prediction of protein–ligand binding affinities (PLAs) is essential for drug discovery and development. Recent advancements suggest that transforming protein–ligand complexes into heterogeneous graph representations may offer a viable solution. However, existing methods ignore the importance of heterogeneous graph augmentation and the complementary information provided by sequence and protein–ligand complex structure modalities, which are crucial for enhancing generalization and robustness. In this study, we propose a multimodal data fusion approach GIF-PLA (meta-path-based enhanced heterogeneous information fusion framework for protein-ligand binding affinity prediction). Protein–ligand binding complexes are represented as heterogeneous graphs with meta-paths, in parallel with protein sequences and ligand simplified molecular input line entry system (SMILES) strings, which are fed into cascaded deep neural networks, respectively. GIF-PLA effectively captures structure-oriented information, encompassing topological interactions and high-order nonlinear relationships, as well as sequence-oriented information. Finally, a late fusion module is used to integrate multilevel information. Comprehensive evaluations demonstrate that GIF-PLA surpasses state-of-the-art methods, achieving a Pearson’s correlation coefficient (Rp) of 0.784 and a root-mean-square error (RMSE) of 1.157 on benchmark data sets. Ablation studies highlight the critical contributions of meta-paths and multimodal fusion. Overall, GIF-PLA shows significant promise in predicting protein–ligand interactions with enhanced reliability.