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ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery
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Abstract
High Resolution Image Download MS PowerPoint Slide Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis–Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.
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Neumann, A., & Klein, R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00719 · Cited by 10
Yin-yang in drug discovery: rethinking de novo design and development of predictive models
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Metadata
- Authors
- Tingle, B. I., Tang, K. G., Castanon, M., Gutierrez, J. J., Khurelbaatar, M., Dandarchuluun, C., Moroz, Y. S., & Irwin, J. J.
- Affiliations
- University of California, San Francisco (US)
- Taras Shevchenko National University of Kyiv (UA)
- Corresponding author
- Irwin, John J.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-02-15
- Volume
- 63
- Issue
- 4
- Pages
- 1166-1176
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC9976280
- OpenAlex
- W4320857994
- Cited by
- 314
- References
- 30
- Citations by year
- 2026: 122 · 2025: 106 · 2024: 62 · 2023: 23
- FWCI
- 44.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (GM133836)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- chemical databases, similarity search, molecular docking, chemical diversity, Bemis-Murcko scaffolds, solvation energy, partial atomic charges, molecular conformation
- MeSH terms
- Ligands, Molecular Conformation, Zinc, Databases, Factual, Molecular Docking Simulation
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