The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
Open accessAbstract
The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This paper clarifies some apparent confusion over the use of the coefficient of determination, R(2), as a measure of model fit and predictive power in QSAR and QSPR modeling. R(2) (or r(2)) has been used in various contexts in the literature in conjunction with training and test data for both ordinary linear regression and regression through the origin as well as with linear and nonlinear regression models. We analyze the widely adopted model fit criteria suggested by Golbraikh and Tropsha ( J. Mol. Graphics Modell. 2002 , 20 , 269 - 276 ) in a strict statistical manner. Shortcomings in these criteria are identified, and a clearer and simpler alternative method to characterize model predictivity is provided. The intent is not to repeat the well-documented arguments for model validation using test data but rather to guide the application of R(2) as a model fit statistic. Examples are used to illustrate both correct and incorrect uses of R(2). Reporting the root-mean-square error or equivalent measures of dispersion, which are typically of more practical importance than R(2), is also encouraged, and important challenges in addressing the needs of different categories of users such as computational chemists, experimental scientists, and regulatory decision support specialists are outlined.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Least Median of Squares Regression
Rousseeuw, P. J. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10477105 · Cited by 3,835
Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735 · cited 1×
“The numerator in the fraction in (1) is the sum of squared residuals (SSR).”
Self-citationTropsha, A., Gramatica, P., & Gombar, V. K. (2003) · QSAR & Combinatorial Science · doi:10.1002/qsar.200390007 · Cited by 2,188
Self-citationBest Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,024
Self-citationKvålseth, T. O. (1985) · The American Statistician · doi:10.1080/00031305.1985.10479448 · Cited by 990
Bayesian Regularization of Neural Networks
Burden, F., & Winkler, D. (2008) · Methods in Molecular Biology™ · doi:10.1007/978-1-60327-101-1_3 · Cited by 621
Self-citationQuantitative Structure–Property Relationship Modeling of Diverse Materials Properties
Le, T., Epa, V. C., Burden, F. R., & Winkler, D. A. (2012) · Chemical Reviews · doi:10.1021/cr200066h · Cited by 565
Self-citationRoy, K., Chakraborty, P., Mitra, I., Ojha, P. K., Kar, S., & Das, R. N. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23231 · Cited by 466
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376
Cited by
Artificial intelligence in drug discovery: recent advances and future perspectives
Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464
Giordano, M. R., Malings, C., Pandis, S. N., Presto, A. A., McNeill, V. F., Westervelt, D. M., Beekmann, M., & Subramanian, R. (2021) · Journal of Aerosol Science · doi:10.1016/j.jaerosci.2021.105833 · Cited by 426
Gramatica, P., & Sangion, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00088 · Cited by 420
Ten simple rules for predictive modeling of individual differences in neuroimaging
Scheinost, D., Noble, S., Horien, C., Greene, A. S., Lake, E. M., Salehi, M., Gao, S., Shen, X., O'Connor, D., Barron, D. S., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.057 · Cited by 381
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01073 · Cited by 337
Metadata
- Authors
- Alexander, D. L. J., Tropsha, A., & Winkler, D. A.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-07-09
- Volume
- 55
- Issue
- 7
- Pages
- 1316-1322
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2294467502
- Cited by
- 745
- References
- 20
- FWCI
- 36.93
- Type
- journal article
- Language
- en
- Open access
- green
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- Alexander, D. L. J., Tropsha, A., & Winkler, D. A. (2015). Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.5b00206
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