%0 Journal Article
%T QSAR Modeling of Some Derivatives of Thiazolidinedione With Antimalarial Properties
%J Frontiers in Chemical Research
%I Ilam University
%Z 2717-2309
%A Asadpour, Saeid
%A Jazayeri Farsani, Sajjad
%A Ghanavati Nasab, Shima
%A Semnani, Abolfazl
%D 2019
%\ 09/01/2019
%V 1
%N 1
%P 17-24
%! QSAR Modeling of Some Derivatives of Thiazolidinedione With Antimalarial Properties
%K Malaria
%K Thiazolidinedione
%K Quantitative structure activity relationship (QSAR)
%K Multiple Linear Regression (MLR)
%K artificial neural network (ANN)
%R 10.22034/fcr.2019.36419
%X Malaria is a serious human health threat that affects the lives of millions of people annually. To this end, the Quantitative structureâ€“activity relationship (QSAR) of 31 thiazolidinedione derivatives were used to predict anti-malarial activity. Multiple linear regression (MLR) model and artificial neural network (ANN) are used for modeling. The best results were obtained for thiazolidinedione derivatives with 5 descriptors. The obtained results indicated that the MLR implemented for thiazolidinedione derivatives with parameters: R2: 0.90, R2adj: 0.88, Q2: 0.89, and RMSE: 2.06. Also, the ANN was used in which the correlation coefficients of the three groups of train, validation, test and total were 0.94, 0.98, 0.99, and 0.95, respectively. Based on the results, a comparison of the quality of the models show that the ANN model has a significantly better predictive capability. ANN establishes a satisfactory relationship between the molecular descriptors and the activity of the studied compounds.
%U http://fcr.ilam.ac.ir/article_36419_adde5e39ad58da7cc2e168cdf60083bf.pdf