The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Comments on the Definition of the Q2 Parameter for QSAR Validation
Abstract
This paper deals with the problem of evaluating the predictive ability of QSAR models and continues the discussion about proper estimates of the predictive ability from an external evaluation set reported in Schüürmann G., Ebert R.-U., et al. External Validation and Prediction Employing the Predictive Squared Correlation Coefficient--Test Set Activity Mean vs Training Set Activity Mean. J. Chem. Inf. Model. 2008, 48, 2140-2145 . The two formulas for calculating the predictive squared correlation coefficient Q2 previously discussed by Schüürmann et al. are one that adopted by the current OECD guidelines about QSAR validation and based on SS (sum of squares) of the external test set referring to the training set response mean and the other based on SS of the external test set referring to the test set response mean. In addition to these two formulas, another formula is evaluated here, based on SS referring to mean deviations of observed values from the training set mean over the training set instead of the external evaluation set.
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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
Self-citationGolbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735
Better Bootstrap Confidence Intervals
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Estimating the error rate of a prediction rule: improvement on cross-validation
Efron, B. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477973 · Cited by 2,300
Principles of QSAR models validation: internal and external
Gramatica, P. (2007) · QSAR & Combinatorial Science · doi:10.1002/qsar.200610151 · Cited by 2,208
Tropsha, A., Gramatica, P., & Gombar, V. K. (2003) · QSAR & Combinatorial Science · doi:10.1002/qsar.200390007 · Cited by 2,188
Cramer, R. D., Bunce, J. D., Patterson, D. E., & Frank, I. E. (1988) · Quantitative Structure-Activity Relationships · doi:10.1002/qsar.19880070105 · Cited by 833
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
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OPERA models for predicting physicochemical properties and environmental fate endpoints
Mansouri, K., Grulke, C. M., Judson, R. S., & Williams, A. J. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0263-1 · Cited by 624
Further exploring r metrics for validation of QSPR models
Ojha, P. K., Mitra, I., Das, R. N., & Roy, K. (2011) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2011.03.011 · Cited by 554
Gramatica, P., & Sangion, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00088 · Cited by 420
Hosseini, R., Newlands, N., Dean, C., & Takemura, A. (2015) · Remote Sensing · doi:10.3390/rs70302752 · Cited by 31
Metadata
- Authors
- Consonni, V., Ballabio, D., & Todeschini, R.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2009-06-15
- Volume
- 49
- Issue
- 7
- Pages
- 1669-1678
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W17944005
- Cited by
- 585
- References
- 18
- FWCI
- 17.06
- Type
- journal article
- Language
- en
- Open access
- closed
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