The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Are the Chemical Structures in Your QSAR Correct?
Abstract
Quantitative structure–activity relationships (QSARs) are used to predict many different endpoints, utilize hundreds, and even thousands of different parameters (or descriptors), and are created using a variety of approaches. The one thing they all have in common is the assumption that the chemical structures used are correct. This research investigates this assumption by examining six public and private databases that contain structural information for chemicals. Molecular fingerprinting techniques are used to determine the error rates for structures in each of the databases. It was observed that the databases had error rates ranging from 0.1 to 3.4%. A case study to predict the n‐octanol/water partition coefficient was also investigated to highlight the effects of these errors in the predictions of QSARs. In this case study, QSARs were developed using both (i) all correct structures and (ii) structures from a database with an error rate of 3.4%. This case study showed how slight errors in chemical structures, such as misplacing a Cl atom or swapping hydroxy and methoxy functional groups on a multiple ring structure, can result in significant differences in the accuracy of the prediction for those chemicals.
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Cites
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107
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
Cited by
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164
Best Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,024
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
Fourches, D., Muratov, E., & Tropsha, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100176x · Cited by 832
Probing the links between in vitro potency, ADMET and physicochemical parameters
Gleeson, M. P., Hersey, A., Montanari, D., & Overington, J. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3367 · Cited by 503
Metadata
- Authors
- Young, D., Martin, T., Venkatapathy, R., & Harten, P.
- Venue
- QSAR & Combinatorial Science
- Publisher
- Wiley
- Published
- 2008-12
- Volume
- 27
- Issue
- 11-12
- Pages
- 1337-1345
- ISSN
- 1611-020X (ISSN-L)
- 1611-0218
- OpenAlex
- W1983464445
- Cited by
- 196
- References
- 2
- FWCI
- 9.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Young, D., Martin, T., Venkatapathy, R., & Harten, P. (2008). Are the Chemical Structures in Your QSAR Correct? QSAR & Combinatorial Science. https://doi.org/10.1002/qsar.200810084
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