ArticleNature communications2023
Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers.
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Who cites it
103 citing papers in PubMed.
- Fragment-Based Discovery of KLK6 and KLK7 Inhibitors.Journal of chemical information and modeling · 2026Article
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- Contemporary design of small-molecule kinase modulators: orthosteric, allosteric and induced-proximity strategies.Nature reviews. Drug discovery · 2026Review
- CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- Biophysical insights into a cryptic ligand site in the hydrophobic core of human PCNA.Biophysical journal · 2026Article
- Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- A graph retrieval-augmented generation pipeline for systematic drug target discovery: validation and application to ocular neovascularization.Briefings in bioinformatics · 2026Article
- Cryptic pockets in proteins: Harnessing conformational dynamics for rational drug design.Acta pharmaceutica Sinica. B · 2026Article
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation.Interdisciplinary sciences, computational life sciences · 2026Article
- Mechanochemical Decoupling of ATP Hydrolysis and RNA Translocation in SARS-CoV-2 nsp13 by the L405D Mutation.bioRxiv : the preprint server for biology · 2026Article
- Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework.Journal of chemical theory and computation · 2026Article
- Sequence-based drug-target binding site pre-training enables cryptic pocket detection and improves binding affinity and kinetics prediction.Journal of cheminformatics · 2026Article
- Sequence-based Drug-Target Binding Site Pretraining Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction.bioRxiv : the preprint server for biology · 2026Article
- Natural Products Beyond Inhibition: A Mechanistic Framework Spanning Pockets, Interfaces, and Kinetic Barriers.Molecules (Basel, Switzerland) · 2026Review
- CryptoBank: A resource for the identification and prediction of cryptic sites in proteins.Science advances · 2026Article
- Integrating computational chemistry and machine learning to predict KRAS mutation-induced resistance.bioRxiv : the preprint server for biology · 2026Article
- Exploring the Structural Basis of Cryptic Pocket Formation Driven by Extensive Protein Conformational Changes in Drug Targets.Journal of chemical theory and computation · 2026Article
43 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly curated dataset of 39 experimentally confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures likely contain cryptic pockets, vastly expanding the potentially druggable proteome.
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