Dual-channel hierarchical interactive learning for the prediction of Protein-Ligand binding affinity.

Protein-ligand binding affinity (PLBA) is a crucial metric in drug screening for identifying potential candidate compounds. In recent years, deep learning-based methods have used representation learning to model interactions within protein-ligand complexes, demonstrating great promise in affinity prediction tasks. Existing studies have considered both intramolecular (covalent) and intermolecular (non-covalent) interactions to some extent. However, these interactions are often treated as independent features, lacking explicit hierarchical dependency modeling, which may lead to insufficient representation of interaction information and ultimately limit the accuracy of affinity predictions. To address this issue, we propose a novel approach-Dual-channel Hierarchical Interactive Learning (DHIL)-to achieve a more comprehensive modeling of protein-ligand interactions. DHIL employs a dual-channel encoding structure to simultaneously learn intramolecular and intermolecular interactions, ensuring the completeness of interaction features. Additionally, we design a hierarchical interactive learning paradigm to facilitate information exchange between these two interaction types at multiple levels, promoting their collaborative modeling. This mechanism mimics the local-to-global working principles of biological systems, enabling a more detailed and holistic representation of protein-ligand interactions. We conduct extensive and comprehensive experiments on a diverse set of benchmark datasets, rigorously evaluating the effectiveness of DHIL. The results demonstrate that DHIL significantly improves PLBA prediction accuracy, outperforming existing methods and further validating its potential in drug discovery and screening tasks. Nevertheless, the proposed framework introduces notable computational overhead due to multi-scale graph construction and cross-level message passing. It also exhibits sensitivity to the quality of input 3D binding conformations, which may affect its robustness in practical applications. These limitations suggest future directions for improving model efficiency and generalizability. To facilitate reproducibility and further research, the complete source code of DHIL has been released at: https://github.com/WZY-0814/DHIL.