Semnan University Press Applied Chemistry Today 2981-2437 6 21 2011 12 22 Application of genetic algorithm with multiple linear regressions for prediction of medicinal activity pyrazole derivatives Application of genetic algorithm with multiple linear regressions for prediction of medicinal activity pyrazole derivatives 35 44 592 10.22075/chem.2017.592 FA Mehdi Nekoe Majid Mohammadhosseini Pourbasheer Maham Journal Article 2017 01 15 Quantitative structure-activity relationship (QSAR) study for prediction of medicinal activity of pyrazole derivatives is developed using structural descriptors and multiple linear regression (MLR) method. Molecular descriptors are selected by genetic algorithm. Then a simple, strong, descriptive and interpretable model with low error and high correlation coefficient is construct. The results illustrated that the linear techniques such as MLR combined with a successful variable selection procedure are capable to generate an efficient QSAR model for predicting the activity of different compounds. This model was used for the prediction of activity values of some medicinal compounds which were not used in the modeling procedure. Quantitative structure-activity relationship (QSAR) study for prediction of medicinal activity of pyrazole derivatives is developed using structural descriptors and multiple linear regression (MLR) method. Molecular descriptors are selected by genetic algorithm. Then a simple, strong, descriptive and interpretable model with low error and high correlation coefficient is construct. The results illustrated that the linear techniques such as MLR combined with a successful variable selection procedure are capable to generate an efficient QSAR model for predicting the activity of different compounds. This model was used for the prediction of activity values of some medicinal compounds which were not used in the modeling procedure. https://chemistry.semnan.ac.ir/article_592_dbe272bab69f8e13f14b405e038deb64.pdf