Traversing Chemical Space with Active Deep Learning: A Computational Framework for Low-data Drug Discovery

Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active deep learning has an untapped potential for low-data drug discovery, as it allows to improve a model iteratively during the screening process by acquiring new data, and to adjust its course along the way. However, several known unknowns exist when it comes to active learning: (a) what the best computational strategies are for chemical space exploration, (b) how active learning holds up to traditional, non-iterative, approaches, and (c) how it should be used in the low-data scenarios typical of drug discovery. These open questions currently limit the wider adoption of active learning in drug discovery. To provide answers, this study simulates a real-world low-data drug discovery scenario, and systematically analyses six active learning strategies combined with two deep learning architectures, on three large- scale molecular libraries. Not only do we show that active learning can achieve up to a six-fold improvement in hit discovery compared to traditional methods, but we also identify the most important determinants of its success in low-data regimes. This study lays the first-in-time foundations for the prospective use of active deep learning for low-data drug discovery and is expected to accelerate its adoption.