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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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Cites
ZINC − A Free Database of Commercially Available Compounds for Virtual Screening
Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058
Self-citationZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224
Self-citationZINC: A Free Tool to Discover Chemistry for Biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S. D., & Coleman, R. G. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3001277 · Cited by 2,611
Self-citationZINC20─A Free Ultralarge-Scale Chemical Database for Ligand Discovery
Irwin, J. J., Tang, K. G., Young, J., Dandarchuluun, C., Wong, B. R., Khurelbaatar, M., Moroz, Y. S., Mayfield, J. W., & Sayle, R. A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00675 · Cited by 1,122
Self-citationUltra-large library docking for discovering new chemotypes
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., et al. (2019) · Nature · doi:10.1038/s41586-019-0917-9 · Cited by 998
Self-citationAn open-source drug discovery platform enables ultra-large virtual screens
Gorgulla, C., Boeszoermenyi, A., Wang, Z., Fischer, P. D., Coote, P. W., Das, K. M., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2117-z · Cited by 677
Self-citationEfficient Exploration of Chemical Space with Docking and Deep Learning
Ying, Y., Yao, K., Repasky, M. P., Leswing, K., Abel, R., Shoichet, B. K., & Jerome, S. V. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00810 · Cited by 480
The next level in chemical space navigation: going far beyond enumerable compound libraries
Hoffmann, T., & Gastreich, M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.02.013 · Cited by 253
A model binding site for testing scoring functions in molecular docking.
Wei, B. Q., Baase, W. A., Weaver, L. H., Matthews, B. W., & Shoichet, B. K. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00777-5 · Cited by 237
Structures of the σ2 receptor enable docking for bioactive ligand discovery
Alon, A., Lyu, J., Braz, J. M., Tummino, T. A., Craik, V., O’Meara, M. J., Webb, C. M., Radchenko, D. S., Moroz, Y. S., Huang, X.-P., et al. (2021) · Nature · doi:10.1038/s41586-021-04175-x · Cited by 211
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- 10.1038/s41586-019-0917-9 · DOI · Paper page
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James, N., & Ballester, P. J. (2026) · Digital Discovery · doi:10.1039/d6dd00399k · Cited by 1
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Baselious, F., Hilscher, S., Handke, L., Bařinka, C., Schutkowski, M., & Sippl, W. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110695 · Cited by 2
Arévalo, J. M. C., & Amorim, J. C. (2022) · Preprints.org · doi:10.20944/preprints202211.0132.v1 · Cited by 1
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Bjerrum, E. J. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-hzddj · Cited by 1
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Shi, Y., Chen, Z., Tang, Y., Zhang, Z., & Chen, B. (2023) · International Conference on Electronic Information Engineering and Computer Science (EIECS) · doi:10.1109/eiecs59936.2023.10435574
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Krosuri, P., & Mohan, M. (2025) · Current Drug Discovery Technologies · doi:10.2174/0115701638359244250411045740
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Romanik, J., Gardella, N., Dematteo, G., Zhao, Y., Moy, T., Brandt, A. L., & Li, Z. (2026) · Journal of Basic and Clinical Physiology and Pharmacology · doi:10.1515/jbcpp-2025-0147
Atyim, E., Atyim, L., Mioc, M., Mioc, A., Şoica, C., Gheorghe, D. R., Negrea-Ghiulai, R., & Paşcalău, N. A. (2026) · Processes · doi:10.3390/pr14111707
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Andersen, T., Nielsen, L., & Sørensen, M. (2026) · Pharmacophore · doi:10.51847/slmtxhwnzi
Kumar, H., Tsang, S. V., Lee, M.-S., Huang, Y.-H., Choi, J., Lotlikar, S. R., Song, J. Y., Toh, A., Choi, Y., Brannan, K. W., et al. (2026) · iScience · doi:10.1016/j.isci.2026.116076
Adebayo, A. S., Ibrahim, B., Abdullahi, A. D., Abubakar, M., Hidayat, A. F. A., Mohamad, S. B., Ibrahim, Y. K. E., & Ibrahim, M. A. (2026) · Experimental Parasitology · doi:10.1016/j.exppara.2026.109156
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Hong, X., Li, P., Zhu, W., Wu, C., Guo, H., Tan, H., Wu, Q., Wu, K., Chen, L., Jia, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.29.728912
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Baskaran, S. P., Tiwari, R., & Samal, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36474-7.00028-4
Panchalingam, S., Jayaraman, M., Jeyaraman, J., & Kasivelu, G. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100438
Alshehri, Z. S. (2026) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-026-10804-7
Chen, C., Lee, L. Y., Wang, T.-Y., Jiang, Y.-Y., Han, C.-H., Hsieh, C.-C., Hung, M.-S., Liao, H., Wu, C.-Y., & Lung, J. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00860-z
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Singh, A. K., Apurv, A., & Evangeline Christina, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49253-2.00029-5
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Huber, F., & Pollmann, J. (2026) · Journal of Cheminformatics · doi:10.1186/s13321-026-01245-y · Cited by 8
Chun, H., Youn, W., Lim, H., Harada, R., Ko, H. Y., Yoon, J., Oh, S. J., Seemann, P., Pepe, J. G., Shoichet, B. K., et al. (2026) · Experimental & Molecular Medicine · doi:10.1038/s12276-026-01757-5
Ahmadkhani, N., Sugden, C., Lee, E., Baiz, C. R., Brown, D., Drummond, N., Snyder, A., Uden, M., & Higgins, A. Z. (2026) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6c06194
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Fahira, A., Shahab, M., Din, Z. U., Li, X., & Jian, X. (2026) · Journal of Genetic Engineering and Biotechnology · doi:10.1016/j.jgeb.2026.100759
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Almeida, R. L., Tikkanen, T., dos Santos, V. S., Veríssimo, G. C., de Azevedo, D. Q., Sivula, T., Maltarollo, V. G., & Pöhner, I. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00098
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Barua, S., Balaji, B., & Balaji, S. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00583
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Choi, J. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27156663
Huang, T.-X., Zhang, W.-L., Gao, M.-C., Yang, Z.-J., Pan, L., Kong, D., Wang, L.-L., & Ding, J.-J. (2026) · Energetic Materials Frontiers · doi:10.1016/j.enmf.2026.07.005
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Silverman, R. B., Xue, F., & Juncosa, J. I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-15862-9.00002-3
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Zhou, Y., Gu, T., Tian, L., Liu, X., & Li, D. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c01583
Khan, A., Zahid, M., Kouidri, W., Mohamed, O. A., Gharaibeh, A. M., Mohamed, L. I., Al-Mansori, A., Elsayed, M. E. M., Mohammad, A., Al-Jabiry, A., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27167089
Huan, T. D., Mahmood, A. U., Chaudhari, H., Mamtani, K., Kim, C., Ramprasad, R., Krishnamoorthy, A., & Patra, A. K. (2026) · Chemistry of Materials · doi:10.1021/acs.chemmater.6c00265
Daudo, H. A., Marinho, E. S., de Oliveira, V. M., Marinho, M. M., & Ribeiro, R. V. (2026) · Molecular Diversity · doi:10.1007/s11030-026-11621-3
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Sahu, S., Rashmi, M., Sharma, S., Sachdeva, N., Murmu, S., Das, R., Shiv, A., & Kumar, S. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5162-9_3
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Kanda, T., Sasaki, R., Okamoto, H., Terai, S., Cwiklowski, C. D., & Venkata, K. C. N. (2026) · Microorganisms · doi:10.3390/microorganisms14091987
Bi, X., Wang, Y., Wang, J., & Liu, C. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1927954
Singhera, F. U., Overhulse, J. M., Lee, T. M., Katz, J. G., Lee, J. S. H., & McKenna, C. E. (2026) · Cancers · doi:10.3390/cancers18182939
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Chung, J., Bhatt, A., Levine, J. E., Lin, Y., Zhao, M., MacKerell, A. D., Jo, S., Kosaraju, S., & Luo, Y. (2026) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-026-00927-x
Pinheiro, D. F., Favarin, J. C., Freire, C. G., Veiga, N., dos Santos, J. M. S. R., Kath, P. C., Zancanaro, V., Corrêa, L. V., da Silva, G. G. P., & Bellaver, E. H. (2026) · Current Pharmaceutical Analysis · doi:10.1016/j.cpan.2026.09.005
Dunárová, A., Gall, M., Matúška, J., Pitoňák, M., Štekláč, M., & Bučinský, L. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7607815
Araripe, D. A., Iljin, M., Bange, H., Price, L., Liu, R., van der Es, D., Heitman, L. H., Peters, D. J. M., & van Westen, G. J. P. (2026) · SLAS DISCOVERY · doi:10.1016/j.slasd.2026.100338
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
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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