ArticleNature communications2025
TAPB: an interventional debiasing framework for alleviating target prior bias in drug-target interaction prediction.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- MEGPNM as a multiscale edge-aware GAT network with hybrid pooling predicting permeability of non-peptidic macrocycles.Communications chemistry · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Rethinking network analysis in ethnopharmacology: a multi-omics and AI roadmap to overcome conceptual and methodological biases.Frontiers in pharmacology · 2026Article
- TAPB: an interventional debiasing framework for alleviating target prior bias in drug-target interaction prediction.Nature communications · 2025Article
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Authors and funding
7 authors.
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Abstract
Drug Target Interaction (DTI) prediction is vital for drug repurposing. Previous DTI studies on BioSNAP and BindingDB datasets often attribute biased predictions to "drug bias," while our work reveals "target prior bias" as the predominant issue. This bias stems from the "prior tendency," characterized by the imbalanced label distribution of targets in the training data. From causal lens, target "prior tendency" is a confounder, causing models trained with P(Y∣D, T) to learn spurious associations between targets and labels rather than genuine interaction mechanisms. In this study, we introduce alleviating Target Prior Bias in Drug-Target Interaction Prediction (TAPB), a novel debiasing framework that employs amino acid randomization, confounder alignment module (CAM), and interventional training to compute P(Y∣D, do(T)) via backdoor adjustment, thereby addressing this bias. TAPB achieves competitive performance over existing approaches, demonstrating enhanced generalization and providing interpretable insights into DTIs.
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