ZINC 15 – Ligand Discovery for Everyone
Open accessAbstract
Many questions about the biological activity and availability of small molecules remain inaccessible to investigators who could most benefit from their answers. To narrow the gap between chemoinformatics and biology, we have developed a suite of ligand annotation, purchasability, target, and biology association tools, incorporated into ZINC and meant for investigators who are not computer specialists. The new version contains over 120 million purchasable "drug-like" compounds--effectively all organic molecules that are for sale--a quarter of which are available for immediate delivery. ZINC connects purchasable compounds to high-value ones such as metabolites, drugs, natural products, and annotated compounds from the literature. Compounds may be accessed by the genes for which they are annotated as well as the major and minor target classes to which those genes belong. It offers new analysis tools that are easy for nonspecialists yet with few limitations for experts. ZINC retains its original 3D roots--all molecules are available in biologically relevant, ready-to-dock formats. ZINC is freely available at http://zinc15.docking.org.
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The experimental data available for training molecular property prediction models is limited compared to the extremely diverse and combinatorially large universe of candidate molecules.
“However, the experimental data available for training such models is limited compared to the extremely diverse and combinatorially large universe of candidate molecules that we would want to make predictions on (Bohacek et al., 1996; Sterling and Irwin, 2015; Lyu et al., 2019; McCloskey et al., 2020).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Sterling and Irwin, 2015
In Results > ZINC Continues To Grow (page unavailable), the paper reports 220 million molecules in ZINC; Results > Activities by Organism Class lists 376,447 distinct compounds active at the stated threshold. This supports a large disparity between catalogued molecules and those with reported activity, but the paper does not establish that experimental data for training molecular-property models are limited relative to the entire combinatorial universe of candidate molecules. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe experimental data available for training models to predict biochemical properties is limited compared to the extremely diverse and combinatorially large universe of candidate molecules.
“However, the experimental data available for training such models is limited compared to the extremely diverse and combinatorially large universe of candidate molecules that we would want to make predictions on”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Sterling and Irwin, 2015
The Results section, “Activities by Organism Class” (Table 1; page unavailable), lists hundreds of thousands of compounds with annotated activity, while Methods, “Source Catalogs” (page unavailable), says other catalogs have unknown biological activity. This supports a gap in available activity data, but the paper does not establish the claim specifically about model-training data or characterize the candidate-molecule universe as combinatorially large. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPublic databases contain experimentally determined bioactivity data for about a million molecules, which represent only a small percentage of commercially available compounds and a negligible fraction of the synthetically accessible chemical space.
“Currently, public databases contain experimentally determined bioactivity data for about a million molecules, which represent only a small percentage of commercially available compounds6 and a negligible fraction of the synthetically accessible chemical space7.”
From Bioactivity descriptors for uncharacterized chemical compounds (2021) · cites this paper as [6]
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Uncertainty quantification: Can we trust artificial intelligence in drug discovery?
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Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment
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How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors
Pogodin, P. V., Lagunin, A. A., Rudik, A. V., Filimonov, D. A., Druzhilovskiy, D. S., Nicklaus, M. C., & Poroikov, V. V. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00133 · Cited by 40 · see citing works
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Elucidating the druggability of the human proteome with eFindSite
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Formatting biological big data for modern machine learning in drug discovery
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Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer
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“Chemical Similarity Searches To identify potentially improved derivatives, high-similarity 3D structural searches were performed on the ZINC15 database [34] using the ROCS program from OpenEye [35].”
MethodWei, X., Zhu, T., Yip, H. F., Fu, X., Jiang, D., Deng, Y., Lu, A., & Cao, D. (2024) · Chemical Science · doi:10.1039/d4sc03580a · Cited by 4 · see citing works
- Authors
- Sterling, T., & Irwin, J. J.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Irwin, John J.
- ORCID
- Irwin, John J.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-11-09
- Volume
- 55
- Issue
- 11
- Pages
- 2324-2337
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC4658288
- OpenAlex
- W1757990252
- Cited by
- 3,224
- References
- 42
- Citations by year
- 2026: 218 · 2025: 311 · 2024: 386 · 2023: 463 · 2022: 456 · 2021: 537 · 2020: 439 · 2019: 188 · 2018: 120 · 2017: 78 · 2016: 25 · 2013: 1
- FWCI
- 77.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (GM71896)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- ZINC database, drug-like molecules, cheminformatics, molecular docking, metabolites, natural products, biological activity
- MeSH terms
- Animals, Genes, Humans, Ligands, Software, User-Computer Interface, Internet, Drug Discovery, Databases, Pharmaceutical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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