The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Best Practices for QSAR Model Development, Validation, and Exploitation
Abstract
After nearly five decades “in the making”, QSAR modeling has established itself as one of the major computational molecular modeling methodologies. As any mature research discipline, QSAR modeling can be characterized by a collection of well defined protocols and procedures that enable the expert application of the method for exploring and exploiting ever growing collections of biologically active chemical compounds. This review examines most critical QSAR modeling routines that we regard as best practices in the field. We discuss these procedures in the context of integrative predictive QSAR modeling workflow that is focused on achieving models of the highest statistical rigor and external predictive power. Specific elements of the workflow consist of data preparation including chemical structure (and when possible, associated biological data) curation, outlier detection, dataset balancing, and model validation. We especially emphasize procedures used to validate models, both internally and externally, as well as the need to define model applicability domains that should be used when models are employed for the prediction of external compounds or compound libraries. Finally, we present several examples of successful applications of QSAR models for virtual screening to identify experimentally confirmed hits.
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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 · cited 1×
“…description of major tenets of QSAR modeling is beyond the scope of this paper; the overview of many popular QSAR modeling techniques including statistical and datamining techniques as well as approaches to descriptor calculations could be found in many reviews and monographs, e.g., [4,5] ).”
MethodZINC − 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
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735
Self-citationOn Outliers and Activity CliffsWhy QSAR Often Disappoints
Maggiora, G. M. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci060117s · Cited by 728
Rational selection of training and test sets for the development of validated QSAR models
Golbraikh, A., Shen, M., Xiao, Z., Xiao, Y.-D., Lee, K.-H., & Tropsha, A. (2003) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1025386326946 · Cited by 712
Self-citationHow not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468
Fourches, D., Barnes, J. C., Day, N. C., Bradley, P., Reed, J. Z., & Tropsha, A. (2010) · Chemical Research in Toxicology · doi:10.1021/tx900326k · Cited by 162
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Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
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
Self-citationMachine learning in chemoinformatics and drug discovery
Lo, Y.-C., Rensi, S. E., Torng, W., & Altman, R. B. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.05.010 · Cited by 960
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
Self-citationCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Metadata
- Authors
- Tropsha, A.
- Venue
- Molecular Informatics
- Publisher
- Wiley
- Published
- 2010-07-12
- Volume
- 29
- Issue
- 6-7
- Pages
- 476-488
- ISSN
- 1868-1743 (ISSN-L)
- 1868-1751
- OpenAlex
- W2128245586
- Cited by
- 2,024
- References
- 7
- FWCI
- 53.43
- Type
- journal article
- Language
- en
- Open access
- closed
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