Computational advances in discovering cryptic pockets for drug discovery

Article Article scientifique Revue de la littérature Computational advances in discovering cryptic pockets for drug discovery eng 2025-02 author Bemelmans Martijn Computer-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. 985779 author Cournia Zoe Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece. author Damm-Ganamet Kelly L Computer-Aided Drug Design, In Silico Discovery, Therapeutics Discovery, Johnson & Johnson Innovative Medicine, 3210 Merryfield Row, San Diego, CA 92121, United States. author Gervasio Francesco Luigi 0000-0003-4831-5039 School of Pharmaceutical Sciences, University of Geneva, Rue Michel Servet 1, Geneva, 1206, Switzerland. Electronic address: francesco.gervasio@unige.ch. 931636 Section des sciences pharmaceutiques (FARMA) author Pande Vineet Computer-Aided Drug Design, In Silico Discovery, Therapeutics Discovery, Johnson & Johnson Innovative Medicine, Turnhoutseweg 30, 2340 Beerse, Belgium. Electronic address: vpande@its.jnj.com. 102975 10.1016/j.sbi.2024.102975 39778412 0959-440X https://linkinghub.elsevier.com/retrieve/pii/S0959440X24002021 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. Allostery Artificial intelligence Cryptic pockets Enhanced sampling Mixed-solvent molecular dynamics Dewey 615 European Commission Allostery in Drug Discovery 956314 ALLODD EU H2020 Swiss National Science Foundation Understanding and Modelling Cryptic Binding Pockets for Biology and Drug Discovery 204795 CH Projects Swiss National Science Foundation MITOpore 216587 CH Programmes Swiss National Science Foundation ABCD Antibodies: Recombinant antibodies for research 203628 CH Programmes 31 Section des sciences pharmaceutiques 17 false 1362 Modélisation biomoléculaire et pharmaceutique Current opinion in structural biology 90 5f218e05-113d-4fed-a179-9204d2bc7a2a Article (Published version) Public 1-s2.0-S0959440X24002021-main.pdf 1557626 application/pdf CC BY-4.0 1fcb58a232dbc7861c42cb1f6ef92c9d92da7ec2527e4ce23cda66dbfe2f590b openAccess Faculté des sciences / Section des sciences pharmaceutiques

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Computational advances in discovering cryptic pockets for drug discovery	Current opinion in structural biology			2025	44	434

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