Jul 2026· Advancement of science· 0 citations· 74 references
Medicine
TL;DR
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.
Abstract
ABSTRACT Mutation‐induced drug resistance challenges both pandemic surveillance and drug discovery. While experimental assays are resource‐intensive, current computational predictions remain limited by the scarcity of 3D mutant protein structures. We present DeepMutDTA, a structure‐independent model pre‐trained on 1.5 million data points to predict drug‐target affinity and uncover underlying interaction mechanisms. However, like other sequence‐based approaches, it often falls short in predicting mutant affinities due to the overwhelming sequence similarity between wild‐type (WT) and mutant (MT) targets. To bridge this gap, we introduce SimSiam‐MuTF, 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. Compared to representative baselines, our model exhibits remarkable robustness across varied sequence identities and unseen data splits, yielding average performance gains of 2.47% (PCC) and 5.10% (SCC) in regression tasks, alongside 4.00% (AUC) and 4.17% (AUPR) in classification tasks. Applications to SARS‐CoV‐2, HIV‐1, and cancer‐related targets highlight its generalization potential and utility in informing therapeutic strategies against drug resistance. Collectively, this robust computational pipeline and fine‐tuning framework deepen our understanding of mutation‐induced resistance and may serve as a powerful platform to accelerate drug discovery against mutant targets.
Antimicrobial resistance (AMR) has become a significant challenge in global public health. With the development of whole-genome sequencing, protein language models, graph neural networks, and multimodal learning, deep learning-based AMR prediction research has rapidly evolved from traditional sequence alignment and rule retrieval to representation learning and genotype-phenotype mapping for high-dimensional heterogeneous data. This review systematically summarizes the research progress in this field from three aspects: antimicrobial resistance gene (ARG) identification and classification, genomic mutation-driven resistance phenotype prediction, and non-WGS multimodal extension. The review shows that deep learning has significantly improved the modeling ability for distantly homologous sequences, complex mutation combinations, and heterogeneous data, driving AMR prediction from “database matching” to “learnable representations,” and from “single-label discrimination” to “multi-task, multi-representation, and multimodal fusion.” However, at the same time, problems such as dataset heterogeneity, inconsistent label standards, class imbalance, lineage mixing, insufficient external generalization, and insufficient interpretability still restrict the clinical application of these models. Future research should further strengthen the construction of basic models, standardized evaluation, mining of interpretable mechanisms, and joint modeling of multi-omics and clinical data to promote AMR prediction from method validation to real-world application.
BoltzOmics is an interactive, open-source platform that integrates Boltz-2, a deep learning model for biomolecular structure prediction, to rapidly assess mutation effects on drug binding, and establishes a practical AI-driven framework for accelerating computational drug discovery and advancing precision medicine research.
K. Ngo, Kermit L. Carraway, Colleen E. Clancy et al.· iScience· 1 citation
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
It is indicated that although many methods report strong performance on standard benchmarks, their effectiveness is often influenced by dataset bias and limited evaluation settings, and most methods exhibit reduced performance in cold-start scenarios, highlighting challenges in generalization.
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.
A two-stage contrastive learning framework integrating drug structures, protein sequences, and Cell Painting morphological profiles into a unified embedding space, which reveals pathway-specific morphological signatures associated with drug targets, providing biologically interpretable insights into drug mechanisms.
