Nashrieh Shimi va Mohandesi Shimi Iran

Nashrieh Shimi va Mohandesi Shimi Iran

QSAR Study of Triazolopyridine Derivatives as PIM Inhibitors Using the Genetic Algorithm-Multiple Linear Regressions

Document Type : Research Article

Authors
Eslam Pourbasheer 1
Zhila Mohajeri Avval 1
Mehdi Nekoei 2
Somayeh Hamidvand 1
1 Department of Chemistry, Payame Noor University, Tehran, I.R. IRAN
2 Department of Chemistry, Faculty of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, I.R. IRAN
Abstract
Quantitative Structure-Activity Relationship (QSAR) was developed for modeling and predicting of the PIM inhibitory activities a data set containing 39 structures of triazolopyridine derivatives with known biological activities. Segmentation the whole dataset into a training set and test set was performed randomly. StepWise (SW) and Genetic Algorithm (GA) techniques with Multiple Linear Regression (MLR) were used to select the most important descriptors and to create the best prediction model. Comparison of the results obtained for SW-MLR and GA-MLR models was showed that GA-MLR model is superior to the SW-MLR model. The robustness and the predictive ability of the final GA-MLR model validated by internal and external statistical validations including Leave-One-Out (LOO) cross-validation, Leave-Group-Out (LGO) cross-validation, Y-randomization and external test set. High agreement between experimental and predicted activity values indicated that GA-MLR model with five variables has good quality and it could be used in design novel compounds with higher PIM inhibitor activity.
Keywords
QSAR
Genetic Algorithms (GA)
Multiple Linear Regressions (MLR)
PIM inhibitors
Triazolopyridine derivatives

Subjects

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