Multimodal Information-Driven Heterogeneous Graph Neural Networks for Protein-Ligand Binding Affinity Prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is essential for efficient drug screening. However, existing methods often inadequately model multimodal molecular interactions and their combined effects on binding affinity. To address this challenge, this study introduces AtomBind, a multimodal information-driven heterogeneous graph neural network framework. First, an atomic-level heterogeneous graph is constructed, integrating sequence information, 3D structure, geometric constraints, and molecular representations generated by pretrained protein and chemical language models. Second, the Intra Encoder utilizes a variational graph autoencoder and equivariant graph neural network architecture to capture complex topological relationships at the molecular scale, addressing short-range atomic dependencies and dynamic structural variations. Finally, the Inter Encoder incorporates graph diffusion convolution and graph Transformer architectures, facilitating cross-molecular information transfer between protein pockets and ligands while simultaneously capturing both global and local intermolecular interaction features. Comparative experiments demonstrate that AtomBind exhibits superior predictive consistency and smaller errors compared to other models on two test sets. Ablation studies and pretrained language model analysis further validate the efficiency and robustness of AtomBind in multimodal information integration and affinity prediction. Additionally, the model demonstrates strong generalization capabilities and broad practical application potential in analyzing protein pockets, intermolecular interactions, and interpretability analysis.