QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions

Abstract

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 descriptors 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.