The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data
Abstract
Quantitative structure–activity relationship (QSAR) techniques have found wide application in the fields of drug design, property modeling, and toxicity prediction of untested chemicals. A rigorous validation of the developed models plays the key role for their successful application in prediction for new compounds. The rm2 metrics introduced by Roy et al. have been extensively used by different research groups for validation of regression‐based QSAR models. This concept has been further advanced here with introduction of scaling of response data prior to computation of rm2. Further, a web application (accessible from http://aptsoftware.co.in/rmsquare/ and http://203.200.173.43:8080/rmsquare/) for calculation of the rm2 metrics has been introduced here. The present study reports that the web application can be easily used for computation of rm2 metrics provided observed and QSAR‐predicted data for a set of compounds are available. Further, scaling of response data is recommended prior to rm2 calculation. © 2013 Wiley Periodicals, Inc.
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Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
Best Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,024
Foye's Principles of Medicinal Chemistry
Foye, W. O., Lemke, T., & Williams, D. A. (2012) · Cited by 1,407
HANSCH, C., MALONEY, P. P., FUJITA, T., & MUIR, R. M. (1962) · Nature · doi:10.1038/194178b0 · Cited by 1,056
Rogers, D., & Hopfinger, A. J. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00020a020 · Cited by 982
Some Relations between Reaction Rates and Equilibrium Constants.
Hammett, L. P. (1935) · Chemical Reviews · doi:10.1021/cr60056a010 · Cited by 866
On Some Aspects of Variable Selection for Partial Least Squares Regression Models
Roy, P. P., & Roy, K. (2008) · QSAR & Combinatorial Science · doi:10.1002/qsar.200710043 · Cited by 793
Self-citationAssessing Model Fit by Cross-Validation
Hawkins, D. M., Basak, S. C., & Mills, D. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci025626i · Cited by 757
Comprehensive Medicinal Chemistry
(1991) · Quantitative Structure-Activity Relationships · doi:10.1002/qsar.19910100410 · Cited by 745
On Two Novel Parameters for Validation of Predictive QSAR Models
Pratim Roy, P., Paul, S., Mitra, I., & Roy, K. (2009) · Molecules · doi:10.3390/molecules14051660 · Cited by 563
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Metadata
- Authors
- Roy, K., Chakraborty, P., Mitra, I., Ojha, P. K., Kar, S., & Das, R. N.
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2013-01-08
- Volume
- 34
- Issue
- 12
- Pages
- 1071-1082
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W2145962544
- Cited by
- 466
- References
- 27
- FWCI
- 46.38
- Type
- journal article
- Language
- en
- Open access
- closed
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