Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Open accessAbstract
Drug molecules consist of a few tens of atoms connected by covalent bonds. How many such molecules are possible in total and what is their structure? This question is of pressing interest in medicinal chemistry to help solve the problems of drug potency, selectivity, and toxicity and reduce attrition rates by pointing to new molecular series. To better define the unknown chemical space, we have enumerated 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens forming the chemical universe database GDB-17, covering a size range containing many drugs and typical for lead compounds. GDB-17 contains millions of isomers of known drugs, including analogs with high shape similarity to the parent drug. Compared to known molecules in PubChem, GDB-17 molecules are much richer in nonaromatic heterocycles, quaternary centers, and stereoisomers, densely populate the third dimension in shape space, and represent many more scaffold types.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The GDB-17 set of organic molecules of up to 17 heavy atoms with a limited number of elements contains 66 billion compounds.
“The GDB-17 (35) set of organic molecules of up to 17 heavy atoms with a limited number of elements (i.e., C, N, O, S and halogens) contains 66 billion compounds”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [35]
Cites
Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success
Lovering, F., Bikker, J. A., & Humblet, C. C. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm901241e · Cited by 4,507 · see citing works
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382 · see citing works
Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167 · see citing works
The Properties of Known Drugs. 1. Molecular Frameworks
Bemis, G. W., & Murcko, M. A. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm9602928 · Cited by 2,501 · see citing works
DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs
Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1126 · Cited by 1,832 · see citing works
Practical graph isomorphism, II
McKay, B. D., & Piperno, A. (2013) · Journal of Symbolic Computation · doi:10.1016/j.jsc.2013.09.003 · Cited by 1,627 · see citing works
PubChem: a public information system for analyzing bioactivities of small molecules
Wang, Y., Xiao, J., Önal-Süzek, T., Zhang, J., Wang, J., & Bryant, S. H. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp456 · Cited by 1,465 · see citing works
Hit and lead generation: beyond high-throughput screening
Bleicher, K., Böhm, H., Müller, K., & Alanine, A. I. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1086 · Cited by 1,054 · see citing works
Computer-based de novo design of drug-like molecules
Schneider, G., & Fechner, U. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1799 · Cited by 956 · see citing works
970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13
Blum, L. C., & Reymond, J. (2009) · Journal of the American Chemical Society · doi:10.1021/ja902302h · Cited by 874 · see citing works
Self-citationNovel trends in high-throughput screening
Mayr, L. M., & Bojanic, D. (2009) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2009.08.004 · Cited by 690 · see citing works
Virtual ligand screening: strategies, perspectives and limitations
Klebe, G. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.05.012 · Cited by 669 · see citing works
Efficient Drug Lead Discovery and Optimization
Jorgensen, W. L. (2009) · Accounts of Chemical Research · doi:10.1021/ar800236t · Cited by 651 · see citing works
Rush, T. S., Grant, J., Mosyak, L., & Nicholls, A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm040163o · Cited by 601 · see citing works
Molecular Shape Diversity of Combinatorial Libraries: A Prerequisite for Broad Bioactivity
Sauer, W., & Schwarz, M. K. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025599w · Cited by 500 · see citing works
Fink, T., & Reymond, J. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600423u · Cited by 457 · see citing works
Self-citationRitchie, T. J., Macdonald, S. J. F., Young, R. J., & Pickett, S. D. (2010) · Drug Discovery Today · doi:10.1016/j.drudis.2010.11.014 · Cited by 451 · see citing works
From atoms and bonds to three-dimensional atomic coordinates: automatic model builders
Sadowski, J., & Gasteiger, J. (1993) · Chemical Reviews · doi:10.1021/cr00023a012 · Cited by 414 · see citing works
Nonleadlikeness and leadlikeness in biochemical screening
Rishton, G. M. (2003) · Drug Discovery Today · doi:10.1016/s1359644602025722 · Cited by 381 · see citing works
Molecular obesity, potency and other addictions in drug discovery
Hann, M. M. (2011) · MedChemComm · doi:10.1039/c1md00017a · Cited by 367 · see citing works
Virtual Exploration of the Small‐Molecule Chemical Universe below 160 Daltons
Fink, T., Bruggesser, H., & Reymond, J. (2005) · Angewandte Chemie International Edition · doi:10.1002/anie.200462457 · Cited by 356 · see citing works
Self-citationReactive compounds and in vitro false positives in HTS
Rishton, G. M. (1997) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01083-0 · Cited by 348 · see citing works
Chemical space as a source for new drugs
Reymond, J., van Deursen, R., Blum, L. C., & Ruddigkeit, L. (2010) · MedChemComm · doi:10.1039/c0md00020e · Cited by 344 · see citing works
Self-citationClemons, P. A., Bodycombe, N. E., Carrinski, H. A., Wilson, J. A., Shamji, A. F., Wagner, B. K., Koehler, A. N., & Schreiber, S. L. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1012741107 · Cited by 343 · see citing works
Geppert, H., Vogt, M., & Bajorath, J. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900419k · Cited by 328 · see citing works
Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database
Reymond, J., & Awale, M. (2012) · ACS Chemical Neuroscience · doi:10.1021/cn3000422 · Cited by 321 · see citing works
Self-citationMolecular Shape and Medicinal Chemistry: A Perspective
Nicholls, A. O., McGaughey, G. B., Sheridan, R. P., Good, A. C., Warren, G. L., Mathieu, M., Muchmore, S. W., Brown, S. P., Grant, J., Haigh, J. A., et al. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm900818s · Cited by 316 · see citing works
Small molecules: the missing link in the central dogma
Schreiber, S. L. (2005) · Nature Chemical Biology · doi:10.1038/nchembio0705-64 · Cited by 306 · see citing works
Structural Diversity of Organic Chemistry. A Scaffold Analysis of the CAS Registry
Lipkus, A. H., Yuan, Q., Lucas, K. A., et al. (2008) · The Journal of Organic Chemistry · doi:10.1021/jo8001276 · Cited by 305 · see citing works
Docking and chemoinformatic screens for new ligands and targets
Kolb, P., Ferreira, R. S., Irwin, J. J., & Shoichet, B. K. (2009) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2009.08.003 · Cited by 189 · see citing works
Lederberg, J., Sutherland, G. L., Buchanan, B. G., Feigenbaum, E. A., ROBERTSON, A. V., Duffield, A. M., & Djerassi, C. (1969) · Journal of the American Chemical Society · doi:10.1021/ja01039a025 · Cited by 169 · see citing works
Hartenfeller, M., & Schneider, G. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_12 · Cited by 160 · see citing works
The enumeration of chemical space
Reymond, J., Ruddigkeit, L., Blum, L. C., & van Deursen, R. (2012) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1104 · Cited by 145 · see citing works
Self-citationMayer, C. R., Cabuil, V., Lalot, T., & Thouvenot, R. (1999) · Angewandte Chemie International Edition · doi:10.1002/(sici)1521-3773(19991216)38:24 · Cited by 139 · see citing works
Clemons, P. A., Wilson, J. A., Dančík, V., Muller, S., Carrinski, H. A., Wagner, B. K., Koehler, A. N., & Schreiber, S. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1015024108 · Cited by 101 · see citing works
Recent developments in automated structure elucidation of natural products
Steinbeck, C. (2004) · Natural Product Reports · doi:10.1039/b400678j · Cited by 97 · see citing works
Cayley, E. (1875) · Berichte der deutschen chemischen Gesellschaft · doi:10.1002/cber.18750080252 · Cited by 81 · see citing works
Recent Trends and Observations in the Design of High-Quality Screening Collections
Renner, S., Popov, M., Schuffenhauer, A., ROTH, H. ‐., Breitenstein, W., Marzinzik, A. L., Lewis, I., Krastel, P., Nigsch, F., Jenkins, J. L., et al. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.15 · Cited by 60 · see citing works
Visualisation and subsets of the chemical universe database GDB-13 for virtual screening
Blum, L. C., van Deursen, R., & Reymond, J. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9436-y · Cited by 59 · see citing works
Self-citationLuethi, E., Nguyen, K. T., Bürzle, M., Blum, L. C., Suzuki, Y., Hediger, M. A., & Reymond, J. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100959g · Cited by 45 · see citing works
Self-citationDiscovery of NMDA Glycine Site Inhibitors from the Chemical Universe Database GDB
Nguyen, K. T., Syed, S., Urwyler, S., Bertrand, S., Bertrand, D., & Reymond, J. (2008) · ChemMedChem · doi:10.1002/cmdc.200800198 · Cited by 41 · see citing works
Self-citationBréthous, L., Garcı́a-Delgado, N., Schwartz, J., Bertrand, S., Bertrand, D., & Reymond, J. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300030r · Cited by 40 · see citing works
Self-citationBlum, L. C., van Deursen, R., Bertrand, S., Mayer, M., Burgi, J. J., Bertrand, D., & Reymond, J. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200410u · Cited by 33 · see citing works
Self-citationGarcı́a-Delgado, N., Bertrand, S., Nguyen, K. T., van Deursen, R., Bertrand, D., & Reymond, J. (2010) · ACS Medicinal Chemistry Letters · doi:10.1021/ml100125f · Cited by 28 · see citing works
Self-citationThe Benefits of Constructing Leads from Fragment Hits
Foloppe, N. (2011) · Future Medicinal Chemistry · doi:10.4155/fmc.11.46 · Cited by 27 · see citing works
Nguyen, K. T., Luethi, E., Syed, S., Urwyler, S., Bertrand, S., Bertrand, D., & Reymond, J. (2009) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2009.04.021 · Cited by 22 · see citing works
Self-citationSchmidt, G. A. (2011) · Chemical & Engineering News · doi:10.1021/cen-v089n003.p038 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107 · see citing works
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966 · see citing works
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
“Further, it is a huge task to find a new drug when there are ~ 10 60 existing drug-like molecules [446].”
BackgroundTwenty years on: the impact of fragments on drug discovery
Erlanson, D. A., Fesik, S. W., Hubbard, R. E., Jahnke, W., & Jhoti, H. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.109 · Cited by 1,000 · see citing works
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911 · see citing works
Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates
Doak, B. C., Over, B., Giordanetto, F., & Kihlberg, J. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.08.013 · Cited by 835 · see citing works
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895 · see citing works
Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720 · see citing works
Self-citationDeep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683 · see citing works
Estimation of the size of drug-like chemical space based on GDB-17 data
Polishchuk, P. G., Madzhidov, T. I., & Varnek, A. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9672-4 · Cited by 619 · see citing works
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651 · see citing works
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works
Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W. J., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., & Curioni, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00765-z · Cited by 465 · see citing works
High-confidence structural annotation of metabolites absent from spectral libraries
Hoffmann, M. A., Nothias, L.-F., Ludwig, M., Fleischauer, M., Gentry, E. C., Witting, M., Dorrestein, P. C., Dührkop, K., & Böcker, S. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01045-9 · Cited by 388 · see citing works
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Machine learning for renewable energy materials
Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311 · see citing works
Huang, B., & von Lilienfeld, O. A. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4964627 · Cited by 294 · see citing works
Towards reproducible computational drug discovery
Schaduangrat, N., Lampa, S., Simeon, S., Gleeson, M. P., Spjuth, O., & Nantasenamat, C. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0408-x · Cited by 208 · see citing works
Artificial intelligence in chemistry and drug design
Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00317-x · Cited by 165 · see citing works
tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143 · see citing works
Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
Yao, K., Herr, J. E., Brown, S. N., & Parkhill, J. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b01072 · Cited by 126 · see citing works
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123 · see citing works
Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00516-0 · Cited by 78 · see citing works
“2): enumerated virtual libraries of stable molecules up to 17 and 13 heavy atoms (GDB17 [83], GDB13 [84]), characterised molecules with varying bioactivities (ChEMBL28 [82]), a synthetically accessible diversity orientated virtual library (Enamine diverse [85]), synthetically accessible targeted virtual libraries (Ena…”
BackgroundMachine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63 · see citing works
Cheng, F., Wang, F., Tang, J., Zhou, Y., Fu, Z., Zhang, P., Haines, J. L., Leverenz, J. B., Gan, L., Hu, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101379 · Cited by 63 · see citing works
Enumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning
Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/c9me00069k · Cited by 43 · see citing works
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
- Authors
- Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L.
- Affiliations
- University of Bern (CH)
- Ecole Polytechnique Fédérale de Lausanne Biomolecular Screening Facility, NCCR Chemical Biology, School of Life Sciences, , 1015 Lausanne, Switzerland
- Corresponding author
- Reymond, Jean-Louis
- ORCID
- Reymond, Jean-Louis
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-10-22
- Volume
- 52
- Issue
- 11
- Pages
- 2864-2875
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2114704115
- Cited by
- 1,505
- References
- 47
- Citations by year
- 2026: 115 · 2025: 202 · 2024: 192 · 2023: 178 · 2022: 174 · 2021: 177 · 2020: 148 · 2019: 100 · 2018: 54 · 2017: 41 · 2016: 36 · 2015: 33 · 2014: 34 · 2013: 19 · 2012: 2
- FWCI
- 30.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by)
- PubMed (publishedVersion)
- BORIS (University Library Bern) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- small organic molecules, chemical space, drug-like molecules, lead compounds, molecular geometry, isomers, quaternary centers, stereoisomers, scaffolds
- MeSH terms
- Computer Simulation, Models, Chemical, Stereoisomerism, Structure-Activity Relationship, Drug Design, Molecular Structure, Prescription Drugs, Databases, Chemical, 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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review