A Unified Hierarchical Multiscale Fusion Framework for Drug-Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery.

Accurately predicting drug-target affinity (DTA) is crucial for accelerating virtual screening and guiding lead optimization in drug discovery. However, current computational approaches face a critical trade-off: interaction-free models lack fine-grained binding details, while interaction-based models overlook higher-order contextual and functional patterns. This limitation hinders both prediction performance and real-world generalization. To overcome this, we propose MF-Net, a unified hierarchical multiscale fusion framework that integrates sequence-, atomic-, and fragment-level representations to model drug-target interactions across complementary scales. MF-Net achieves state-of-the-art performance on the PDBBind v2016 benchmark and demonstrates strong early enrichment across multiple virtual screening datasets. Additionally, ADP-Glo assays confirm that the MF-Net-guided virtual screening pipeline identifies seven novel nanomolar inhibitors targeting hematopoietic progenitor kinase 1 (HPK1). Among them, one compound achieves sub-nanomolar activity (IC50 = 0.41 nM), outperforming the positive control inhibitor Sunitinib. These results demonstrate that MF-Net not only excels on standard benchmarks but also delivers tangible lead discovery outcomes, underscoring its practical value for structure-based drug design.