The drug development pipeline is a costly and lengthy process. Identifying high-quality “hit” compounds—those with high potency, selectivity, and favorable metabolic properties—at the earliest stages is important for reducing cost and accelerating the path to clinical trials. For the last decade, scientists have looked to machine learning to make this initial screening process more efficient.
Scientist tackles key roadblock for AI in drug discovery
