8 research outputs found
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><sup>2</sup><sub>F1</sub>, <i>Q</i><sup>2</sup><sub>F2</sub>, <i>Q</i><sup>2</sup><sub>F3</sub>, CCC, and <i>r<sub>m</sub></i><sup>2</sup> (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<sub>m</sub></i><sup>2</sup> 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<sub>m</sub></i><sup>2</sup> provides
the most stringent criterion (especially with Δ<i>r</i><sub><i>m</i></sub><sup>2</sup> 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,
î—»CH<sub>2</sub> 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><sup>2</sup><sub>F1</sub>, <i>Q</i><sup>2</sup><sub>F2</sub>, <i>Q</i><sup>2</sup><sub>F3</sub>, CCC, and <i>r<sub>m</sub></i><sup>2</sup> (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<sub>m</sub></i><sup>2</sup> 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<sub>m</sub></i><sup>2</sup> provides
the most stringent criterion (especially with Δ<i>r</i><sub><i>m</i></sub><sup>2</sup> 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><sup>2</sup><sub>F1</sub>, <i>Q</i><sup>2</sup><sub>F2</sub>, <i>Q</i><sup>2</sup><sub>F3</sub>, CCC, and <i>r<sub>m</sub></i><sup>2</sup> (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<sub>m</sub></i><sup>2</sup> 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<sub>m</sub></i><sup>2</sup> provides
the most stringent criterion (especially with Δ<i>r</i><sub><i>m</i></sub><sup>2</sup> 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><sup>2</sup><sub>F1</sub>, <i>Q</i><sup>2</sup><sub>F2</sub>, <i>Q</i><sup>2</sup><sub>F3</sub>, CCC, and <i>r<sub>m</sub></i><sup>2</sup> (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<sub>m</sub></i><sup>2</sup> 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<sub>m</sub></i><sup>2</sup> provides
the most stringent criterion (especially with Δ<i>r</i><sub><i>m</i></sub><sup>2</sup> 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><sup>2</sup><sub>F1</sub>, <i>Q</i><sup>2</sup><sub>F2</sub>, <i>Q</i><sup>2</sup><sub>F3</sub>, CCC, and <i>r<sub>m</sub></i><sup>2</sup> (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<sub>m</sub></i><sup>2</sup> 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<sub>m</sub></i><sup>2</sup> provides
the most stringent criterion (especially with Δ<i>r</i><sub><i>m</i></sub><sup>2</sup> 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><sup>2</sup><sub>F1</sub>, <i>Q</i><sup>2</sup><sub>F2</sub>, <i>Q</i><sup>2</sup><sub>F3</sub>, CCC, and <i>r<sub>m</sub></i><sup>2</sup> (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<sub>m</sub></i><sup>2</sup> 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<sub>m</sub></i><sup>2</sup> provides
the most stringent criterion (especially with Δ<i>r</i><sub><i>m</i></sub><sup>2</sup> 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><sup>2</sup><sub>F1</sub>, <i>Q</i><sup>2</sup><sub>F2</sub>, <i>Q</i><sup>2</sup><sub>F3</sub>, CCC, and <i>r<sub>m</sub></i><sup>2</sup> (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<sub>m</sub></i><sup>2</sup> 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<sub>m</sub></i><sup>2</sup> provides
the most stringent criterion (especially with Δ<i>r</i><sub><i>m</i></sub><sup>2</sup> at highest tolerated value of 0.2) of external validation,
which may be adopted in the case of regulatory decision support processes