Aug 2026· Advancement of science· 0 citations· 47 references
Medicine
TL;DR
This work proposes MF‐Net, a unified hierarchical multiscale fusion framework that integrates sequence‐, atomic‐, and fragment‐level representations to model drug–target interactions across complementary scales and demonstrates strong early enrichment across multiple virtual screening datasets.
Abstract
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.
ABSTRACT Unifying drug‐target affinity prediction and targeted molecular design within a single interpretable framework remains challenging. Many sequence‐based affinity and design methods rely on global target representations without explicitly modeling binding regions, leading to site‐level ambiguity in both screening and design. By contrast, structure‐based methods require high‐quality structural data and are poorly suited to dynamic targets. In this study, a new model named MolDBG is proposed as a unified site‐aware framework that combines affinity prediction, binding‐site identification, and affinity‐conditioned molecular generation within a single architecture. With binding‐site supervision, MolDBG prioritizes interaction‐critical residues before learning drug‐target representations, reducing false positives from misaligned binding sites and improving interpretability. MolDBG achieves competitive performance across all three tasks while enabling site‐specific affinity prediction and interpretable binding‐site discovery. The framework generalizes to structurally elusive targets, including cryptic pockets and intrinsically disordered proteins. Overall, these results demonstrate that MolDBG is a promising framework for molecular design and screening.
Gang Luo, Qian-Qian Zhang, Chen-Hao Wang et al.· Advancement of science· 0 citations
Accurate prediction of drug-target binding affinity (DTA) is a key task in virtual screening. However, current computational methods face a key challenge: sequence-based approaches often fail to capture critical spatial information, while structure-based models rely on computationally expensive 3D coordinates, which restrict their scalability. To address this issue, we propose StructuraDTA, a novel multimodal framework that adopts an implicit structure modeling strategy. Instead of using static protein folding data, our method encodes drug molecular graphs via Graph Isomorphism Networks (GINs) to capture fine-grained topological features. Meanwhile, we optimize protein representations by integrating probabilistic structural priors into a pretrained language model, which effectively simulates thermodynamic conformational flexibility without relying on explicit 3D structural data. A bidirectional cross-attention mechanism is then used to dynamically align these heterogeneous feature modalities. Comprehensive evaluations on the Davis and KIBA benchmark datasets show that StructuraDTA stably outperforms state of-the-art comparison methods. Importantly, the model exhibits strong robustness in cold-start scenarios, and can accurately predict binding affinities for previously unseen drugs and targets. By retaining the predictive performance of structure based models while maintaining the high inference efficiency of sequence-based methods, we provide an accurate and scalable solution to accelerate genome-scale drug discovery research.
Junlin Xu, Ye Yuan, Menglong Hu et al.· IEEE journal of biomedical a...· 0 citations
Drug-drug interaction (DDI) event prediction is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing computational approaches are limited by their inability to jointly model the heterogeneous mechanisms underlying DDIs, which span molecular structure, pharmacodynamic function, and network-mediated relations. To address this limitation, we introduce M2DDI, a unified framework for dynamic multimodal fusion in DDI prediction. M2DDI utilizes a Mixture-of-Experts architecture, with each expert dedicated to a distinct pharmacological modality. A novel prior-enhanced dual-path gating strategy adaptively selects relevant experts for each drug pair by integrating mechanism-matched feature queries and ATC-based biomedical priors, thereby aligning expert selection with underlying pharmacological mechanisms and addressing the challenge of data incompleteness. Empirical evaluation on benchmark datasets demonstrates that M2DDI achieves state-of-the-art performance, particularly in new drug scenarios. Additional robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions. Analysis of expert selection patterns further confirms alignment with established pharmacological mechanisms. These results establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction. The code is available at: https://github.com/RunqingXuCn/M2DDI.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
SimSiam‐MuTF is introduced, a novel fine‐tuning framework to enhance the detection of resistance variants by explicitly aligning latent embedding distances with the corresponding shifts in binding affinity between WT and MT targets, which deepen the understanding of mutation‐induced resistance.
Xiaowen Hu, Pan Zhang, Shangqian Wu et al.· Advancement of science· 0 citations
Virtual screening (VS) on small molecules aims to identify promising drug candidates against protein targets from expansive chemical libraries by balancing the core requirements of accurate scoring and efficient search against the inherent trade‐off between accuracy and speed. This survey provides a comprehensive review of how Artificial Intelligence and Machine Learning (AI/ML) are redefining this landscape across three critical dimensions. First, we examine the evolution of AI‐driven scoring functions, which utilize AI/ML models to capture complex structure–activity relationships from massive biochemical datasets, significantly enhancing structure‐ and ligand‐based evaluations beyond traditional heuristics. Second, we summarize the emergence of efficient search algorithms that iteratively prioritize informative compounds to reduce search efforts by orders of magnitude. Third, we review the paradigm shift toward generative molecular design, making VS transition from screening fixed libraries to the
de novo
generation of molecules optimized for specific structural contexts and multi‐objective properties. This review outlines the transition toward end‐to‐end, adaptive discovery systems that ensure computational hits are biologically potent, structurally optimized, and synthetically accessible.
Yifei Wang, Nupur Bansal, Shiyun Wa et al.· WIREs Computational Molecula...· 0 citations
Tuberculosis (TB) caused by
Mycobacterium tuberculosis
(Mtb) remains a major global health threat, particularly with rising drug resistance. Protein kinase B (PknB), an essential mycobacterial Ser/Thr kinase absent in humans, is a promising therapeutic target. This study describes the use of an integrated computational workflow to identify natural small molecules with high potential to bind PknB. Structure‐based virtual screening, machine‐learning algorithms, and deep‐learning bioactivity prediction identified six compounds with high predicted pIC
50
values. The AI‐based ADMET assessment showed promising pharmacokinetic and toxicity profiles, and the redocking and residue‐interaction analyses suggested strong binding affinities and interactions. The all‐atom molecular dynamics simulations showed the stability of the protein–ligand complexes over 1000 ns. CNP0362879, CNP0343061, and CNP0413118 were identified as the most favorable binders by MM/GBSA free‐energy calculations. DFT and QM/MM analyses also characterized the electronic properties related to the molecular reactivity and binding. Network pharmacology linked the prioritized compounds with therapeutically relevant targets and pathways. This AI‐integrated multiscale approach offers an efficient platform to accelerate natural‐product‐based anti‐TB drug discovery and identifies promising PknB inhibitors for further experimental validation.
