Decrypting cryptic pockets with physics-based simulations and artificial intelligence

Cryptic pockets are promising targets for drug discovery that greatly expand the druggable proteome. In particular, they can provide opportunities to target proteins previously thought to be "undruggable" due to a lack of pockets in structures of the ground state. However, their transient and hidden nature renders them difficult to detect through conventional experimental screening methods. Recent advances in computational methodologies and resources have greatly enhanced our ability to identify and characterize such elusive pockets. This review highlights key developments in computational approaches, including physics-based molecular dynamics simulations, artificial intelligence-driven models, and hybrid strategies that integrate both to enhance cryptic pocket discovery and functional interpretation.