ReviewBioinformatics advances2025
Recent computational advances in the identification of cryptic binding sites for drug discovery.
Review in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Fragment-Based Discovery of KLK6 and KLK7 Inhibitors.Journal of chemical information and modeling · 2026Article
- CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- The influence of ligands on AlphaFold3 prediction of cryptic pockets.Communications biology · 2026Article
- Decrypting cryptic pockets with physics-based simulations and artificial intelligence.Current opinion in structural biology · 2026Review
- UniPocket: Unified Ligand and Cryptic Pocket Prediction from Protein Language Model Embeddings.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Cryptic ligand binding sites, defined as binding pockets that exist in the ligand-bound state of a protein but not in its Results: This review article looks at the current state of cryptic binding site research, highlighting advancements in both molecular dynamic (MD) methods and machine learning (ML) methods to predict and utilize these sites. Availibilty and Implementation: MD methods include the use of Markov State Models, Enhanced Sampling, and other methods such as Cosolvent MD, while ML methods utilize Support Vector Machine, Random Forest, and Neural Networks. Here, we discuss case studies for both methods and their overlaps, providing insight into the future and the limitations faced. Compared to MD methods, ML methods are often reported to be more cost- and time-effective. However, a limited number of datasets are available for training these ML methods. Integrating MD with ML methods promises to expand our ability to predict and validate new cryptic binding sites that can be evaluated for druggability.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.