ArticleJournal of chemical theory and computation2023
SiteFerret: Beyond Simple Pocket Identification in Proteins.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- Computational Modeling of PROTAC Ternary Complexes as Ensembles Using SILCS-xTAC.Journal of chemical information and modeling · 2026Article
- Computational Ligand-Binding Site Prediction.Advances in experimental medicine and biology · 2026Review
- Discovering Uncharted Binding Pockets on E3 Ligases Leads to the Identification of FBW7 Allosteric Modulators.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Innovative Approaches in Molecular Docking for the Discovery of Novel Inhibitors Against Alzheimer's Disease.Current Alzheimer research · 2025Review
- Databases of ligand-binding pockets and protein-ligand interactions.Computational and structural biotechnology journal · 2024Review
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
- Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.Journal of chemical information and modeling · 2024Article
- GSK-3β Allosteric Inhibition: A Dead End or a New Pharmacological Frontier?International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel method for the automatic detection of pockets on protein molecular surfaces. The algorithm is based on an ad hoc hierarchical clustering of virtual probe spheres obtained from the geometrical primitives used by the NanoShaper software to build the solvent-excluded molecular surface. The final ranking of putative pockets is based on the Isolation Forest method, an unsupervised learning approach originally developed for anomaly detection. A detailed importance analysis of pocket features provides insight into which geometrical (clustering) and chemical (amino acidic composition) properties characterize a good binding site. The method also provides a segmentation of pockets into smaller subpockets. We prove that subpockets are a convenient representation to pinpoint the binding site with great precision. SiteFerret is outstanding in its versatility, accurately predicting a wide range of binding sites, from those binding small molecules to those binding peptides, including difficult shallow sites.
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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.