@article { author = {Heidarimoghadam, Rashid and Mortazavi, Seyede Shima and Farmany, Abbas}, title = {Prediction of IC50 of 2,5-diaminobenzophenone organic derivatives using informatics-aided genetic algorithm}, journal = {Iranian chemical communication}, volume = {7}, number = {Issue 1, pp. 1-89, Serial No. 22}, pages = {39-51}, year = {2019}, publisher = {}, issn = {2423-4958}, eissn = {2345-4806}, doi = {10.30473/icc.2018.4932}, abstract = {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 obtained by the models it was shown 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. Applying the extended model to a dataset of 20 compounds demonstrate the reliability and accuracy of the model. Comparing three models revealed the superiority of the L-M ANN to predict the pIC50 of 2,5-diaminobenzophenones derivatives.}, keywords = {P. falciparum malaria,antimalarial compounds,2,5-diaminobenzophenones,QSAR}, url = {https://icc.journals.pnu.ac.ir/article_4932.html}, eprint = {https://icc.journals.pnu.ac.ir/article_4932_4aa9b8e65178146b3fd3b2ad1df7d236.pdf} }