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Drug Res (Stuttg) 2017; 67(08): 476-484
DOI: 10.1055/s-0043-108553
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

QSAR Study of Artemisinin Analogues as Antimalarial Drugs by Neural Network and Replacement Method

Fatemeh Abbasitabar
1   Young Researchers Club, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
,
Vahid Zare-Shahabadi
2   Department of Chemistry, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
› Author Affiliations
Further Information

Publication History

received 25 December 2016

accepted 01 April 2017

Publication Date:
30 May 2017 (online)

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Abstract

Quantitative structure–activity relationship (QSAR) models were derived for 179 analogues of artemisinin, a potent antimalarial agent. Molecular descriptors derived solely from molecular structure were used to represent molecular structure. Utilizing replacement method, a subset of 11 descriptors was selected. General regression neural network (GRNN) was used to construct the nonlinear QSAR models in all stages of study. The relative standard error percent in antimalarial activity predictions for the training set by the application of cross-validation (RMSE-CV) was 0.43, and for test set (RMSEtest) was 0.51. GRNN analysis yielded predicted activities in the excellent agreement with the experimentally obtained values (R2training = 0.967 and R2test = 0.918). The mean absolute error for the test set was computed as 0.4115.

Key words

quantitative-structure-activity relationship - generalized regression neural network - artemisinin - antimalarial activity - replacement method
 

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