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

Predictive <i>in silico</i> Modeling of Ionic Liquids toward Inhibition of the Acetyl Cholinesterase Enzyme of <i>Electrophorus electricus</i>: A Predictive Toxicology Approach

Chemicals are the essential components of the industry for maneuvering the required need of the living ecosystem. Ionic liquids are a group of promising novel chemicals with potential usefulness toward various industrial applications, although they are not entirely devoid of hazardous outcomes. The present study is an attempt to investigate the chemical attributes of a wide variety of 292 ionic liquids toward their inhibitory potential of acetyl cholinesterase enzyme of electric eel through the development of predictive regression and classification-based quantitative mathematical models in the light of the OECD guidelines. Molecular docking studies have additionally corroborated the results. Hydrophilicity, hydrophobicity, branching, and positively charged <i>N</i>-species were observed to be the major chemical contributors to such toxicity. The docking studies chiefly portrayed the π-cationic type interaction of the cationic N⁺ atom with the Phe288, Phe290, and Trp23 residues of the acyl binding pocket to be responsible for enzyme inhibition

Identifying natural compounds as multi-target-directed ligands against Alzheimer’s disease: an <i>in silico</i> approach

<p>Alzheimer’s disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at <a href="http://teqip.jdvu.ac.in/QSAR_Tools/" target="_blank">http://teqip.jdvu.ac.in/QSAR_Tools/</a>. The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a <i>confidence estimation</i> approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database (<a href="https://www.ibscreen.com/natural-compounds" target="_blank">https://www.ibscreen.com/natural-compounds</a>). Further, the natural compounds that were categorized as ‘actives’ in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.</p

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