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 costly. 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.