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Updated: Jun 3, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
Computational advances in discovering cryptic pockets for drug discovery
Martijn P Bemelmans1, Zoe Cournia2, Kelly L Damm-Ganamet3
1Computer-Aided Drug Design, In Silico Discovery, Therapeutics Discovery, Johnson & Johnson Innovative Medicine, Turnhoutseweg 30, 2340 Beerse, Belgium; School of Pharmaceutical Sciences, University of Geneva, Rue Michel Servet 1, Geneva, 1206, Switzerland.
Abstract:
A number of promising therapeutic target proteins have been considered "undruggable" due to the lack of well-defined ligandable pockets. Substantial research in protein dynamics has elucidated the existence of "cryptic" pockets that only exist transiently and become favorable for binding in the presence of a ligand. These pockets provide an avenue to target challenging proteins, inspiring the development of multiple computational methods. This review highlights established cryptic pocket modeling approaches like mixed solvent molecular dynamics and recent applications of enhanced sampling and AI-based methods in therapeutically relevant proteins.
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