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

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.