ABSTRACT 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.
Shuo Liu, Xiang Zhang, Haixia Feng et al.· Advancement of science· 0 citations
Transition paths between metastable protein states encode both equilibrium structure statistics and dynamical connectivity yet are costly to obtain with molecular dynamics (MD) and remain challenging to emulate with machine learning. Here, we present TPS-Flow, a physics-guided flow-based generative framework for end point-conditioned conformational path sampling between predefined protein states (not equilibrium ensembles). TPS-Flow represents structures as residue-level SE(3) transforms, uses a spatiotemporal gated attention encoder to learn a flow-matching interpolation velocity field from MD trajectories, and incorporates optional energy and structure-aware constraints together with a short physics-based relaxation step. Across a mycobacterial membrane transporter, a monomeric protein, a protein-protein complex, and a soluble enzyme, TPS-Flow preserves residue-wise fluctuation patterns with damped amplitudes and occupies TICA-projected conformational corridors consistent with reference MD, provides conformational coverage complementary to finite reference MD sampling and generates intermediates with reference-comparable docking scores while reducing model size and computational cost compared to a state-of-the-art trajectory generator (MDGen). In an out-of-distribution structural generalization test using PN-subdomain mutants, TPS-Flow preserved fold continuity and wild-type-like global RMSF patterns when conditioned on AF3-derived mutant end point structures, thereby bridging atomistic simulation and deep generative modeling of protein transition paths.
Kai Xu, Likun Zhao, Yanan Tian et al.· Journal of Chemical Informat...· 0 citations
CoBind is presented, a multitask deep learning framework that jointly predicts RNA–compound interactions and nucleotide-level binding-site probabilities within a unified architecture and provides complementary nucleotide-level binding-site localization, supporting a site-aware view of RNA–ligand recognition under distribution shift.
Shihang Wang, Lin Wang, Wei Zhao et al.· Journal of Medicinal Chemist...· 0 citations