Deep learning for low-data drug discovery: hurdles and opportunities

25 January 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches – which are notoriously ‘data-hungry’ – might fail to live up to their promise. Developing novel approaches to leverage the power of deep learning in low-data scenarios is sparking great attention, and future developments are expected to propel the field further. This minireview provides an overview of recent low-data-learning approaches in drug discovery, analyzing their hurdles and advantages. Finally, we venture to provide a forecast of future research directions in low-data learning for drug discovery.


data augmentation
transfer learning
reinforcement learning
active learning
multimodal learning
multi-task learning
molecular machine learning
de novo design
molecular property prediction


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