QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions
Joint Authors
Beheshti, Abu al-Qasim
Nekoei, Mahdi
Vahdani, Sadat
Pourbasheer, Islam
Source
Journal of Saudi Chemical Society
Issue
Vol. 20, Issue 3 (31 May. 2016), pp.282-290, 9 p.
Publisher
Publication Date
2016-05-31
Country of Publication
Saudi Arabia
No. of Pages
9
Main Subjects
Abstract EN
A quantitative structure–activity relationship (QSAR) was performed to analyze antimalarial activities of 68 urea derivatives using multiple linear regressions (MLR).
QSAR analyses were performed on the available 68 IC50 oral data based on theoretical molecular descriptors.
A suitable set of molecular d escriptors were calculated to represent the molecular structures of compounds, such as constitutional, topological, geometrical, electrostatic and quantum-chemical descriptors.
The important descriptors were selected with the aid of the genetic algorithm (GA) method.
The obtained model was validated using leave-one-out (LOO) cross-validation; external test set and Y-randomization test.
The root mean square errors (RMSE) of the training set, and the test set for GA–MLR model were calculated to be 0.314 and 0.486, the square of correlation coefficients (R2) were obtained 0.801 and 0.803, respectively.
Results showed that the predictive ability of the model was satisfactory, and it can be used for designing similar group of antimalarial compounds.
American Psychological Association (APA)
Beheshti, Abu al-Qasim& Pourbasheer, Islam& Nekoei, Mahdi& Vahdani, Sadat. 2016. QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. Journal of Saudi Chemical Society،Vol. 20, no. 3, pp.282-290.
https://search.emarefa.net/detail/BIM-683812
Modern Language Association (MLA)
Pourbasheer, Islam…[et al.]. QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. Journal of Saudi Chemical Society Vol. 20, no. 3 (May. 2016), pp.282-290.
https://search.emarefa.net/detail/BIM-683812
American Medical Association (AMA)
Beheshti, Abu al-Qasim& Pourbasheer, Islam& Nekoei, Mahdi& Vahdani, Sadat. QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. Journal of Saudi Chemical Society. 2016. Vol. 20, no. 3, pp.282-290.
https://search.emarefa.net/detail/BIM-683812
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 289-290
Record ID
BIM-683812