ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance

Qichang Zhao, Haochen Zhao, Linyuan Guo, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li and Jianxin Wang ()
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Qichang Zhao: Central South University
Haochen Zhao: Central South University
Linyuan Guo: Central South University
Kai Zheng: Central South University
Yajie Li: Central South University
Qiao Ling: Central South University
Jing Tang: University of Helsinki
Yaohang Li: Old Dominion University
Jianxin Wang: Central South University

Nature Communications, 2025, vol. 16, issue 1, 1-22

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-61745-7

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