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Cited by 4Computational Drug Discovery MethodsProtein Degradation and InhibitorsMachine Learning in Bioinformatics

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A meta learning and task adaptive approach for drug target affinity prediction

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Wan, Mengxuan, Zhao, Yanpeng, Zhang, Yixin, Xu, Huiyan, Yi, Duoyun, Zan, Peng, He, Song, Bo, Xiaochen·2026·Nature Communications
DOI 10.1038/s41467-026-70554-5
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Abstract

Accurate and robust prediction of drug-target affinity (DTA) plays a critical role in drug discovery. While deep learning has advanced DTA prediction, existing methods struggle with limited training data and poor generalization. In this study, we propose AdaMBind, a novel DTA prediction model based on meta-learning framework with an adaptive task module designed for low-data scenarios. It employs a dynamic “easy-to-hard” task scheduling mechanism to enhance training efficiency and robustness. Experimental results on three benchmark datasets demonstrate that AdaMBind outperforms 8 baseline models in predicting affinity for unseen targets, particularly under few-shot conditions. Under stringent data constraints, the model successfully identifies high-affinity compounds for ESR and TP53, achieving outstanding virtual screening performance. Furthermore, when applied to inhibitor discovery against FLT3 for acute myeloid leukemia, AdaMBind successfully identified candidate compounds with potent inhibitory activity, as verified by preliminary experimental assays. In summary, AdaMBind provides a robust framework for few-shot DTA prediction.

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Metadata

Authors
Wan, M., Zhao, Y., Zhang, Y., Xu, H., Yi, D., Zan, P., He, S., & Bo, X.
Affiliations
  • Shanghai University (CN)
  • Academy of Military Medical Sciences (CN)
Corresponding author
  • Zan, Peng
  • He, Song
  • Bo, Xiaochen
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2026-03-10
Volume
17
Issue
1
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-026-70554-5
OpenAlex
W7134885640
Cited by
4
References
1
Citations by year
2026: 4
FWCI
14.52
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
drug-target affinity prediction, meta-learning, task adaptation, few-shot learning, task scheduling, virtual screening, drug discovery, FLT3 inhibitors, acute myeloid leukemia, ESR, TP53
MeSH terms
Deep Learning, Predictive Learning Models, Adaptive Algorithms, Prediction Algorithms, Humans, Leukemia, Myeloid, Acute, Tumor Suppressor Protein p53, fms-Like Tyrosine Kinase 3, Drug Discovery

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