From algorithms to systems: integrating computation into drug discovery

AI presents an opportunity to envisage a new approach to drug discovery, but cultural and technological changes are required. The exponential growth in computational drug discovery tools requires solutions that enable researchers to access scalable and robust capabilities more easily. Data generation is usually the slowest and most expensive part of the design cycle; we advocate for a rigorous application of statistical methods focussing on learning efficiency from data over absolute predictive accuracy of models. Automation also plays a critical role in enabling rapid, high-quality data generation. Focussing on modular interoperable automated units with more attractive economics will drive much wider adoption.