ArticleNature communications2025
ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Bridging antiviral drug discovery with a large language model-powered framework.Communications biology · 2026Article
- A progressive fine-tuning framework with dynamic parameter selection for low-resource peptide-GPCR interaction prediction.Briefings in bioinformatics · 2026Article
- Contrastive learning in both structure and function spaces improve drug-target interaction prediction.BMC bioinformatics · 2026Article
- DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.Bioinformatics advances · 2026Article
- CS-DTA: a language model-driven framework for robust drug-target affinity prediction under strict cold-start scenarios.Frontiers in chemistry · 2026Article
- ColdstartMHDTI: integrating biomolecular pretraining and attention-based heterogeneous graph learning for drug-target interaction prediction.Frontiers in chemistry · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability compared to structure-based methods in virtual screening. ColdstartCPI also excels in sparse and low-similarity data conditions, demonstrating its potential in data-limited settings. Our results are validated through literature search, molecular docking, and binding free energy calculations. Overall, ColdstartCPI offers a perspective on sequence-based drug design, presenting a promising tool for drug discovery.
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