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This inherent data limitation clashes with the requirements of many powerful deep learning models, which typically require large datasets. Here, we present Meta-Mol, a novel few-shot learning framework based on Bayesian Model-Agnostic Meta-Learning. Meta-Mol introduces a novel atom-bond graph isomorphism encoder that captures molecular structure information at the atomic and bond levels. This representation is further enhanced by a Bayesian meta-learning strategy, allowing for task-specific parameter adaptation and reducing overfitting risks. Additionally, a hypernetwork is employed to dynamically adjust weight updates across tasks, facilitating more complex posterior estimation. Our results demonstrate that Meta-Mol significantly outperforms existing models on several benchmarks, providing a robust solution to address data scarcity in drug discovery.<\/jats:p>","DOI":"10.1093\/bib\/bbaf408","type":"journal-article","created":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T11:33:44Z","timestamp":1753616024000},"source":"Crossref","is-referenced-by-count":3,"title":["Pushing the boundaries of few-shot learning for low-data drug discovery with a Bayesian meta-learning hypernetwork framework"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6823-1882","authenticated-orcid":false,"given":"Jiacai","family":"Yi","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology , Deya Road, Changsha, Hunan 410073 ,","place":["PR 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