The DOI matches its Crossref record.
Cheminformatics Analysis of Organic Substituents: Identification of the Most Common Substituents, Calculation of Substituent Properties, and Automatic Identification of Drug-like Bioisosteric Groups
Abstract
A large set of more than 3 million molecules was processed to find all the organic substituents contained in the set and to identify the most common ones. During the analysis, 849 574 unique substituents were found. Extrapolated to the number of known organic molecules, this result suggests that about 3.1 million substituents are known. Based on these findings the size of virtual organic chemistry space accessible using currently known synthetic methods is estimated to be between 10(20) and 10(24) molecules. The extracted substituents were characterized by calculated electronic, hydrophobic, steric, and hydrogen bonding properties as well as by the drug-likeness index. Various possible applications of such a large database of drug-like substituents characterized by calculated properties are discussed and illustrated by reference to a Web-based tool for automatic identification of bioisosteric groups.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Ertl, P., & Schuffenhauer, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-8 · Cited by 1,939
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
Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720
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
Fragment-Based Approaches in Drug Discovery and Chemical Biology
Scott, D. E., Coyne, A. G., Hudson, S. A., & Abell, C. (2012) · Biochemistry · doi:10.1021/bi3005126 · Cited by 460
Synthon-based ligand discovery in virtual libraries of over 11 billion compounds
Sadybekov, A. A., Sadybekov, A. V., Liu, Y., Iliopoulos-Tsoutsouvas, C., Huang, X.-P., Pickett, J., Houser, B., Patel, N., Tran, N. K., Tong, F., et al. (2022) · Nature · doi:10.1038/s41586-021-04220-9 · Cited by 417
“Standard libraries for high-throughput screening (HTS) 10 and virtual ligand screening (VLS) 11–13 have been historically limited to fewer than 10 million available compounds, which is a small fraction of the enormous chemical space, estimated to be 10 20 to 10 60 drug-like compounds 14,15 .”
BackgroundLu, S.-H., Wu, J. W., Liu, H.-L., Zhao, J.-H., Liu, K.-T., Chuang, C.-K., Lin, H.-Y., Tsai, W.-B., & Ho, Y. (2011) · Journal of Biomedical Science · doi:10.1186/1423-0127-18-8 · Cited by 204
Autonomous Molecular Design: Then and Now
Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103
Fragment Informatics and Computational Fragment‐Based Drug Design: An Overview and Update
Sheng, C., & Zhang, W. (2013) · Medicinal Research Reviews · doi:10.1002/med.21255 · Cited by 80
Metadata
- Authors
- Ertl, P.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 2002-12-06
- Volume
- 43
- Issue
- 2
- Pages
- 374-380
- ISSN
- 0095-2338 (ISSN-L)
- 1520-5142
- OpenAlex
- W2166478947
- Cited by
- 292
- References
- 0
- FWCI
- 4.55
- Type
- journal article
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.