ShallowBench: Benchmarking Generative Drug Design Models on Shallow-Pocket Targets
Researchers have developed ShallowBench, a new benchmark designed to evaluate generative AI models used in drug design, specifically focusing on targets with shallow binding pockets. Existing models perform poorly on these challenging targets, which are common in areas like oncology. ShallowBench, comprising 5,780 targets, aims to drive innovation in AI architectures and loss functions to improve drug discovery for historically difficult-to-target proteins. AI
IMPACT Highlights limitations in current generative AI for drug design, spurring development of new models for challenging biological targets.