Real External Predictivity of QSAR Models: How To Evaluate It? Comparison of Different Validation Criteria and Proposal of Using the Concordance Correlation Coefficient
| العنوان: | Real External Predictivity of QSAR Models: How To Evaluate It? Comparison of Different Validation Criteria and Proposal of Using the Concordance Correlation Coefficient |
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| المؤلفون: | Paola Gramatica, Nicola Chirico |
| المصدر: | Journal of Chemical Information and Modeling. 51:2320-2335 |
| بيانات النشر: | American Chemical Society (ACS), 2011. |
| سنة النشر: | 2011 |
| مصطلحات موضوعية: | Models, Molecular, Measure (data warehouse), Quantitative structure–activity relationship, External validation, simulations, criteria, concordance correlation coefficient, Correlation coefficient, General Chemical Engineering, Quantitative Structure-Activity Relationship, General Chemistry, 010501 environmental sciences, Library and Information Sciences, 01 natural sciences, 0104 chemical sciences, Computer Science Applications, 010404 medicinal & biomolecular chemistry, Concordance correlation coefficient, Simulated data, Statistics, 0105 earth and related environmental sciences, Mathematics |
| الوصف: | The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. As a measure for such evaluation the OECD guidelines have proposed the predictive squared correlation coefficient Q(2)(F1) (Shi et al.). However, other validation criteria have been proposed by other authors: the Golbraikh-Tropsha method, r(2)(m) (Roy), Q(2)(F2) (Schüürmann et al.), Q(2)(F3) (Consonni et al.). In QSAR studies these measures are usually in accordance, though this is not always the case, thus doubts can arise when contradictory results are obtained. It is likely that none of the aforementioned criteria is the best in every situation, so a comparative study using simulated data sets is proposed here, using threshold values suggested by the proponents or those widely used in QSAR modeling. In addition, a different and simple external validation measure, the concordance correlation coefficient (CCC), is proposed and compared with other criteria. Huge data sets were used to study the general behavior of validation measures, and the concordance correlation coefficient was shown to be the most restrictive. On using simulated data sets of a more realistic size, it was found that CCC was broadly in agreement, about 96% of the time, with other validation measures in accepting models as predictive, and in almost all the examples it was the most precautionary. The proposed concordance correlation coefficient also works well on real data sets, where it seems to be more stable, and helps in making decisions when the validation measures are in conflict. Since it is conceptually simple, and given its stability and restrictiveness, we propose the concordance correlation coefficient as a complementary, or alternative, more prudent measure of a QSAR model to be externally predictive. |
| تدمد: | 1549-960X 1549-9596 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e095cf86a2b7b15769cabe9d481501d https://doi.org/10.1021/ci200211n |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....7e095cf86a2b7b15769cabe9d481501d |
| قاعدة البيانات: | OpenAIRE |
Find this issue from the American Chemical Society | تدمد: | 1549960X 15499596 |
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