Journal
DRUG RESEARCH
Volume 67, Issue 8, Pages 476-484Publisher
GEORG THIEME VERLAG KG
DOI: 10.1055/s-0043-108553
Keywords
quantitative-structure-activity relationship; generalized regression neural network; artemisinin; antimalarial activity; replacement method
Funding
- Islamic Azad University Marvdasht Branch
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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 (R-training(2) = 0.967 and R-test(2) = 0.918). The mean absolute error for the test set was computed as 0.4115.
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