Abstract
Background: Quantitative structure–activity relationship (QSAR) models could provide both statistical significance and useful chemical insights for drug design. The QSAR method has found applications for predicting diverse properties of organic compounds, including antiviral activities, toxicities and biological activities. In this work, a quantitative structure-activity relationship was utilized for the prediction of allosteric BRAF (V600E) inhibitory activities.
Methods: A data set which contains 54 molecules was classified into training and test sets. Stepwise (SW) and genetic algorithm (GA) methods were employed for feature selection. The models were validated using the cross-validation and external test set. Results: Results showed that the GA approach is a more powerful technique than SW for the selection of suitable descriptors. The squared cross-validated correlation coefficient for leave-one-out of 0.702 and squared correlation coefficient of 0.793 was obtained for the training set compounds by GA–MLR model. Conclusion: The obtained GA–MLR model could be applied as a worthwhile model for designing similar groups of the mentioned inhibitors.Keywords: QSAR, multiple linear regression, stepwise, genetic algorithm, BRAF (V600E) inhibitors, inhibitory activity.
Current Computer-Aided Drug Design
Title:Prediction of Activities of BRAF (V600E) Inhibitors by SW-MLR and GA-MLR Methods
Volume: 13 Issue: 3
Author(s): Parinaz Pargolghasemi, Mir Saleh Hoseininezhad-Namin and Aiyoub Parchehbaf Jadid*
Affiliation:
- Department of Chemistry, Ardabil Branch Islamic Azad University, Ardabi,Iran
Keywords: QSAR, multiple linear regression, stepwise, genetic algorithm, BRAF (V600E) inhibitors, inhibitory activity.
Abstract: Background: Quantitative structure–activity relationship (QSAR) models could provide both statistical significance and useful chemical insights for drug design. The QSAR method has found applications for predicting diverse properties of organic compounds, including antiviral activities, toxicities and biological activities. In this work, a quantitative structure-activity relationship was utilized for the prediction of allosteric BRAF (V600E) inhibitory activities.
Methods: A data set which contains 54 molecules was classified into training and test sets. Stepwise (SW) and genetic algorithm (GA) methods were employed for feature selection. The models were validated using the cross-validation and external test set. Results: Results showed that the GA approach is a more powerful technique than SW for the selection of suitable descriptors. The squared cross-validated correlation coefficient for leave-one-out of 0.702 and squared correlation coefficient of 0.793 was obtained for the training set compounds by GA–MLR model. Conclusion: The obtained GA–MLR model could be applied as a worthwhile model for designing similar groups of the mentioned inhibitors.Export Options
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Cite this article as:
Pargolghasemi Parinaz , Hoseininezhad-Namin Saleh Mir and Jadid Parchehbaf Aiyoub *, Prediction of Activities of BRAF (V600E) Inhibitors by SW-MLR and GA-MLR Methods, Current Computer-Aided Drug Design 2017; 13 (3) . https://dx.doi.org/10.2174/1573409913666170303113812
DOI https://dx.doi.org/10.2174/1573409913666170303113812 |
Print ISSN 1573-4099 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6697 |
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