ProDTI: Prior-Guided Positive-Unlabeled Learning for Robust Drug-Target Interaction Prediction

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ID: 331598
2026
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Abstract
Abstract Motivation Accurate Drug-Target Interaction (DTI) prediction is critical for drug discovery. While deep learning advances this field, the fundamental lack of verified negative interactions inevitably causes biased risk estimation. Although formulating DTI prediction as a Positive-Unlabeled (PU) learning problem is theoretically sound, directly applying PU learning to deep models suffers from early-stage optimization instability and decision boundary ambiguity. Results We propose ProDTI, a prior-guided PU learning framework integrating domain knowledge into the PU objective. We construct a hybrid prior combining explicit biochemical similarities with implicit semantic representations from large-scale pre-trained models. This prior drives two synergistic mechanisms: soft-label distillation to stabilize the optimization trajectory, and hard-anchor regularization to explicitly penalize reliable negatives and sharpen decision boundaries. Evaluated on five benchmarks, ProDTI consistently outperforms state-of-the-art baselines, exhibiting superior robustness in challenging cold-start scenarios and providing a reliable tool for computational drug discovery. Availability and Implementation Source code and data are available at https://github.com/WangYu-AI4SCI/ProDTI. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7220838013 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yu Wang, Haoxiang Zhang, Ziye Zhu, Qiang Wei, Yun Li
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag712
URL
https://doi.org/10.1093/bioinformatics/btag712
Keywords Keywords not found

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