Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis

A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.

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“… [a] Split model M1; Others two split models are listed in the original publication and are available in QSARINS‐Chem.…”

Section: Methodsmentioning

confidence: 99%

QSARINS‐chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS

2014

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“…It has always remained the objective of QSAR modelers to improve the quality of predictions, ie, to achieve a low degree of predicted residuals for query compounds. To accomplish this aim, several workers have used consensus modeling approach when multiple models are available. The consensus models integrating all validated individual models (IM) were found to be the most externally predictive in many studies .…”

Section: Introductionmentioning

confidence: 99%

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

Roy

Ambure

Kar

et al. 2018

Journal of Chemometrics

106

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Quantitative structure-activity/property/toxicity relationship (QSAR/QSPR/ QSTR) models are effectively employed to fill data gaps by predicting a given response from known structural features or physicochemical properties of new query compounds. The performance of a model should be assessed based on the quality of predictions checked through diverse validation metrics, which confirm the reliability of the developed QSAR models along with the acceptability of their prediction quality for untested compounds. There is an ongoing effort by QSAR modelers to improve the quality of predictions by lowering the predicted residuals for query compounds. In this endeavor, consensus models integrating all validated individual models were found to be more externally predictive than individual models in many previous studies. The objective of this work has been to explore whether the quality of predictions of external compounds can be enhanced through an "intelligent" selection of multiple models. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. Our approach is different from the previous ones in that none of the previously reported methods considered selection of predictive models in a query compound specific way while at the same time using all or most of the valid models for the total set of query chemicals. We have implemented our approach in a software tool that is freely available via the web http://teqip.jdvu.ac.in/QSAR_ Tools/ and http://dtclab.webs.com/software-tools.QSAR models (based on OECD guidelines 4 ) can be used to "generate" data which can be used for regulatory decisions. The importance of QSAR has also greatly enhanced in recent years due to their potential application in challenging areas like modeling of responses for chemical mixtures, bioactive peptides, nanomaterials, and others. 5 The accuracy and reliability of predictions of QSAR models are very important in their application in regulatory decision support process. Validation is the method which checks reliability and precision of predictions of QSAR models. 6 Although, several techniques including cross-validation, Y-scrambling, and test set validation are commonly employed, in general, external validation is considered as the gold standard for checking predictive ability of QSAR models. 7 However, some group of scientists think cross-validation is better suited for checking predictive ability of QSAR models in order to avoid loss of information from splitting of the data set into training and test sets. 8 They have also argued that the test of predictive ability of QSAR models from a single training-test split is biased and insufficient. 8 Although a comparison of suitability of cross-validation vs external validation for judging predictive ability of QSAR models is a matter of debate, the importance of a test to check quality of predictions ...

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“…These results suggest that the BIOWIN battery approach is appropriate for fragrance materials, whereas BIOWIN6 is less accurate and easily misclassifies not readily biodegradable compounds as readily biodegradable (false‐positives). In a broad sense this can be explained by the greater structural and response information “weighted” by combining 2 different models in the battery approach, and it lends support to consensus modeling .…”

Section: Resultsmentioning

confidence: 90%

“…However, BIOWIN6 set with threshold 0.3 consistently lacks predictivity for not readily biodegradable compounds, independently of the prediction set. A way to overcome predictive instability highlighted for kNN-DRAGON and BIOWIN6 models may be the use of consensus-like and combinatorial approaches, which minimize model uncertainty and errors caused by limitations of the applicability domain [30][31][32][33][34][35][36].…”

Section: Additional Validation Of Predictivity and Applicability Domainmentioning

confidence: 99%

Modeling ready biodegradability of fragrance materials

Ceriani

Papa

Kovarich

et al. 2015

Enviro Toxic and Chemistry

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In the present study, quantitative structure activity relationships were developed for predicting ready biodegradability of approximately 200 heterogeneous fragrance materials. Two classification methods, classification and regression tree (CART) and k-nearest neighbors (kNN), were applied to perform the modeling. The models were validated with multiple external prediction sets, and the structural applicability domain was verified by the leverage approach. The best models had good sensitivity (internal ≥80%; external ≥68%), specificity (internal ≥80%; external 73%), and overall accuracy (≥75%). Results from the comparison with BIOWIN global models, based on group contribution method, show that specific models developed in the present study perform better in prediction than BIOWIN6, in particular for the correct classification of not readily biodegradable fragrance materials.

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Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis

Cited by 80 publications

References 21 publications

QSARINS‐chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS

QSARINS‐chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS

Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models?

Modeling ready biodegradability of fragrance materials

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