Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions

Phenotypic drug discovery (PDD) identifies new drugs by observing the effects of compounds on living systems without prior knowledge of their targets. Advances in biological data and machine learning have made PDD more systematic and data-driven. This review outlines a computational framework, including phenotype representations, key tools, and public datasets. It also discusses major challenges and strategies to improve PDD's efficiency and translational potential, offering a practical guide for researchers in the field.