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• W2891905011 endingPage "11406" @default.
• W2891905011 startingPage "11392" @default.
• W2891905011 abstract "Quantitative structure-activity relationship (QSAR) models have long been used for making predictions and data gap filling in diverse fields including medicinal chemistry, predictive toxicology, environmental fate modeling, materials science, agricultural science, nanoscience, food science, and so forth. Usually a QSAR model is developed based on chemical information of a properly designed training set and corresponding experimental response data while the model is validated using one or more test set(s) for which the experimental response data are available. However, it is interesting to estimate the reliability of predictions when the model is applied to a completely new data set (true external set) even when the new data points are within applicability domain (AD) of the developed model. In the present study, we have categorized the quality of predictions for the test set or true external set into three groups (good, moderate, and bad) based on absolute prediction errors. Then, we have used three criteria [(a) mean absolute error of leave-one-out predictions for 10 most close training compounds for each query molecule; (b) AD in terms of similarity based on the standardization approach; and (c) proximity of the predicted value of the query compound to the mean training response] in different weighting schemes for making a composite score of predictions. It was found that using the most frequently appearing weighting scheme 0.5-0-0.5, the composite score-based categorization showed concordance with absolute prediction error-based categorization for more than 80% test data points while working with 5 different datasets with 15 models for each set derived in three different splitting techniques. These observations were also confirmed with true external sets for another four endpoints suggesting applicability of the scheme to judge the reliability of predictions for new datasets. The scheme has been implemented in a tool Prediction Reliability Indicator available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/, and the tool is presently valid for multiple linear regression models only." @default.
• W2891905011 created "2018-09-27" @default.
• W2891905011 creator A5000323690 @default.
• W2891905011 creator A5079122928 @default.
• W2891905011 creator A5087029647 @default.
• W2891905011 date "2018-09-19" @default.
• W2891905011 modified "2023-10-17" @default.
• W2891905011 title "How Precise Are Our Quantitative Structure–Activity Relationship Derived Predictions for New Query Chemicals?" @default.
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• W2891905011 doi "https://doi.org/10.1021/acsomega.8b01647" @default.
• W2891905011 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6645132" @default.
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• W2891905011 hasPublicationYear "2018" @default.
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