Prediction of IC50 of 2,5-diaminobenzophenone organic derivatives antimalarial compounds using informatics-aided genetic algorithm

In the present paper, informatics-aided quantitative structure activity relationship (QSAR) models using genetic algorithm-partial least square (GA-PLS), genetic algorithm-Kernel partial least square (KPLS), and Levenberg-Marquardt artificial neural network (LM ANN) approach were constructed to access the antimalarial activity (pIC50) of 2,5-diaminobenzophenone derivatives. Comparison of errors and correlation coefficients was obtained by the models as it illustrated that the LM ANN approach works with a high correlation coefficient and low prediction error. This model was applied to the prediction of pIC50 values of 2,5-diaminobenzophenone derivatives.