NciaNet: A Non-Covalent Interaction-Aware Graph Neural Network for the Prediction of Protein-Ligand Interaction in Drug Discovery

Precise quantification of protein-ligand interaction is critical in early-stage drug discovery. Artificial intelligence (AI) has gained massive popularity in this area, with deep-learning models used to extract features from ligand and protein molecules. However, these models often fail to capture intermolecular non-covalent interactions, the primary factor influencing binding, leading to lower accuracy and interpretability. Moreover, such models overlook the spatial structure of protein-ligand complexes, resulting in weaker generalization. To address these issues, we propose Non-covalent Interaction-aware Graph Neural Network (NciaNet), a novel method that effectively utilizes intermolecular non-covalent interactions and 3D protein-ligand structure. Our approach achieves excellent predictive performance on multiple benchmark datasets and outperforms competitive baseline models in the binding affinity task, with the benchmark core set v.2016 achieving an RMSE of 1.208 and an R of 0.833, and the core set v.2013 achieving an RMSE of 1.409 and an R of 0.805, under the high-quality refined v.2016 training conditions. Importantly, NciaNet successfully learns vital features related to protein-ligand interactions, providing biochemical insights and demonstrating practical utility and reliability. However, despite these strengths, there may still be limitations in generalizability to unseen protein-ligand complexes, suggesting potential avenues for future work.