The future of machine learning within target identification: Causality, reversibility, and druggability

To increase the rate of success within the drug development pipeline, the most effective strategy would be to improve the choice of nominated targets at the preclinical stages. Ironically, this is where machine learning has had only a modest application within biomedical science. Here, we outline an emerging strategy, advocated for by ourselves and others, leveraging diverse datasets and experiments across population and functional genetics, single cell technologies, and structural biology.