VI Jornades IET "Bretxa salarial i desigualtats de gènere en el mercat de treball"

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes

Predictive Chemometric Modeling and Three-Dimensional Toxicophore Mapping of Diverse Organic Chemicals Causing Bioluminescent Repression of the Bacterium Genus <i>Pseudomonas</i>

Classification and regression-based quantitative structure–activity relationship (QSAR) as well as three-dimensional (3D) toxicophore models were developed for toxicity prediction of 104 organic chemicals causing bioluminescent repression of the bacterium genus <i>Pseudomonas</i> isolated from industrial wastewater. Statistically significant and interpretable in silico models were obtained using linear discriminant analysis (classification), genetic partial least-squares (regression), and 3D toxicophore models. The QSAR and toxicophore models were scrupulously validated internally as well as externally along with the randomization test to avoid the possibilities of chance correlation. Features such as octanol–water partition coefficient, third-order branching, CH₂ fragment or unsaturation, and the presence of a higher number of electronegative atoms (specifically halogen atoms) and their contribution toward hydrophobicity have been identified as major responsible structural attributes for higher toxicity from the developed in silico models. The present approaches can provide rich information in the context of virtual screening of relevant chemical libraries for aquatic toxicity prediction

Comparative Studies on Some Metrics for External Validation of QSPR Models

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes

Comparative Studies on Some Metrics for External Validation of QSPR Models

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes

Comparative Studies on Some Metrics for External Validation of QSPR Models

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes

Comparative Studies on Some Metrics for External Validation of QSPR Models

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes

Comparative Studies on Some Metrics for External Validation of QSPR Models

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes

Comparative Studies on Some Metrics for External Validation of QSPR Models

Quantitative structure–property relationship (QSPR) models used for prediction of property of untested chemicals can be utilized for prioritization plan of synthesis and experimental testing of new compounds. Validation of QSPR models plays a crucial role for judgment of the reliability of predictions of such models. In the QSPR literature, serious attention is now given to external validation for checking reliability of QSPR models, and predictive quality is in the most cases judged based on the quality of predictions of property of a single test set as reflected in one or more external validation metrics. Here, we have shown that a single QSPR model may show a variable degree of prediction quality as reflected in some variants of external validation metrics like <i>Q</i>²_F1, <i>Q</i>²_F2, <i>Q</i>²_F3, CCC, and <i>r_m</i>² (all of which are differently modified forms of predicted variance, which theoretically may attain a maximum value of 1), depending on the test set composition and test set size. Thus, this report questions the appropriateness of the common practice of the “classic” approach of external validation based on a single test set and thereby derives a conclusion about predictive quality of a model on the basis of a particular validation metric. The present work further demonstrates that among the considered external validation metrics, <i>r_m</i>² shows statistically significantly different numerical values from others among which CCC is the most optimistic or less stringent. Furthermore, at a given level of threshold value of acceptance for external validation metrics, <i>r_m</i>² provides the most stringent criterion (especially with Δ<i>r</i>_<i>m</i>² at highest tolerated value of 0.2) of external validation, which may be adopted in the case of regulatory decision support processes