ArticleBioinformatics (Oxford, England)2025
SP-DTI: subpocket-informed transformer for drug-target interaction prediction.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Multiscale cross modal fusion and cascaded signal propagation for drug target interaction prediction.iScience · 2026Article
- VCCV: conservative transcriptomic corroboration for measurement prioritization of computational drug-target hypotheses.Bioinformatics (Oxford, England) · 2026Article
- Beyond the canonical: The role of post-transcriptional regulation in drug-target interaction prediction.PLoS computational biology · 2026Article
- KANPM-DTA: improving drug-target affinity prediction with Kolmogorov-Arnold networks and pretrained models.Briefings in bioinformatics · 2026Article
- CMA-DTI: a cross-modal fusion and attentive interaction network for interpretable drug-target interaction prediction.Frontiers in bioinformatics · 2026Article
- InfoMedex: drug-drug interaction prediction via a multimodal CNN-transformer model.Frontiers in drug safety and regulation · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- The structure-preserving spectral graph neural network for dual kinase inhibitors and synergy scoring in gastric cancer.NPJ digital medicine · 2025Article
- Decoupled dual-granularity rebalanced pyramid network for drug-target interaction prediction.Briefings in bioinformatics · 2025Article
- GS-DTI: a graph-structure-aware framework leveraging large language models for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Enhanced information cross-attention fusion for drug-target binding affinity prediction.PeerJ. Computer science · 2025Article
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Authors and funding
5 authors.
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Abstract
motivationDrug-target interaction (DTI) prediction is crucial for drug discovery, significantly reducing costs and time in experimental searches across vast drug compound spaces. While deep learning has advanced DTI prediction accuracy, challenges remain: (i) existing methods often lack generalizability, with performance dropping significantly on unseen proteins and cross-domain settings; and (ii) current molecular relational learning often overlooks subpocket-level interactions, which are vital for a detailed understanding of binding sites.
resultsWe introduce SP-DTI, a subpocket-informed transformer model designed to address these challenges through: (i) detailed subpocket analysis using the Cavity Identification and Analysis Routine for interaction modeling at both global and local levels, and (ii) integration of pre-trained language models into graph neural networks to encode drugs and proteins, enhancing generalizability to unlabeled data. Benchmark evaluations show that SP-DTI consistently outperforms state-of-the-art models, achieving an area under the receiver operating characteristic curve of 0.873 in unseen protein settings, an 11% improvement over the best baseline. AVAILABILITY AND IMPLEMENTATION: The model scripts are available at https://github.com/Steven51516/SP-DTI.
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