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Pushing the boundaries of few-shot learning for low-data drug discovery with a Bayesian meta-learning hypernetwork framework

July 2025 Jiacai Yi, Dejun Jiang, Chengkun Wu, Xiaochen Zhang, Weixing He, Wentao Zhao, Dongsheng Cao Briefings in Bioinformatics
Few-Shot Learning Bayesian Meta-Learning Low-Data Drug Discovery

Hunting for candidate compounds with favorable pharmacological, toxicological, and pharmacokinetic properties in drug discovery is essentially a low-data problem, as data acquisition is both challenging and expensive. We propose DS-Meta, a Bayesian meta-learning hypernetwork framework that generates task-adaptive model parameters conditioned on limited support examples. By integrating directed message-passing neural networks with molecular descriptors and a Bayesian hypernetwork for uncertainty-aware task adaptation, DS-Meta pushes the boundaries of few-shot molecular property prediction, consistently outperforming existing methods on multiple benchmark datasets across diverse low-data drug discovery scenarios.