ArticleNature communications2024
Comprehensive detection and characterization of human druggable pockets through binding site descriptors.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- RBM15 in leukemogenesis: an emerging regulator at the interface of RNA biology and hematopoiesis.Trends in cancer · 2026Review
- Proteome-wide identification of the druggable CRBN interactome.Nature biotechnology · 2026Article
- Novel perspectives on PPARγ regulation: from SPPARMs to the emerging role of lncRNAs in metabolic disorders.International journal of obesity (2005) · 2026Review
- Selection of Novel DNA Aptamers against HPV16 Biomarker, E6: AnACS omega · 2026Article
- CryptoBank: A resource for the identification and prediction of cryptic sites in proteins.Science advances · 2026Article
- ProCV: A 3D similarity grouping method for enhanced protein pocket recognition and ligand interaction analysis.iScience · 2025Article
- Navigating a 1E+60 Chemical Space of Peptide/Peptoid Oligomers.Molecular informatics · 2025Article
- Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.PloS one · 2025Article
- Comparative evaluation of methods for the prediction of protein-ligand binding sites.Journal of cheminformatics · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.
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