Graph Neural Network-Based Drug-Target Interaction Prediction with Multi-Scale Molecular Fingerprints

摘要

Predicting drug-target interactions (DTIs) is fundamental for drug discovery but remains challenging due to the vast chemical and protein space. We present MolGraphDTI, a graph neural network framework that integrates multi-scale molecular representations — atomic-level graphs, pharmacophore-level substructure graphs, and protein contact maps — through a hierarchical attention mechanism. On the BindingDB benchmark, MolGraphDTI achieves an AUC of 0.967 and an AUPR of 0.952, outperforming state-of-the-art methods by 3.2%. Ablation studies confirm that each representation scale contributes complementary information. Applied to SARS-CoV-2 main protease (Mpro), the model identifies 12 novel inhibitor candidates, 4 of which show IC₅₀ < 1 μM in enzymatic assays, validating the practical utility of the approach.