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Heidarimoghadam, Rashid, Mortazavi, Seyede Shima, Farmany, Abbas. (1397). Prediction of IC50 of 2,5-diaminobenzophenone organic derivatives using informatics-aided genetic algorithm. فصلنامه علمی زیست فناوری گیاهان زراعی, 7(Issue 1, pp. 1-89, Serial No. 22), 39-51. doi: 10.30473/icc.2018.4932
Rashid Heidarimoghadam; Seyede Shima Mortazavi; Abbas Farmany. "Prediction of IC50 of 2,5-diaminobenzophenone organic derivatives using informatics-aided genetic algorithm". فصلنامه علمی زیست فناوری گیاهان زراعی, 7, Issue 1, pp. 1-89, Serial No. 22, 1397, 39-51. doi: 10.30473/icc.2018.4932
Heidarimoghadam, Rashid, Mortazavi, Seyede Shima, Farmany, Abbas. (1397). 'Prediction of IC50 of 2,5-diaminobenzophenone organic derivatives using informatics-aided genetic algorithm', فصلنامه علمی زیست فناوری گیاهان زراعی, 7(Issue 1, pp. 1-89, Serial No. 22), pp. 39-51. doi: 10.30473/icc.2018.4932
Heidarimoghadam, Rashid, Mortazavi, Seyede Shima, Farmany, Abbas. Prediction of IC50 of 2,5-diaminobenzophenone organic derivatives using informatics-aided genetic algorithm. فصلنامه علمی زیست فناوری گیاهان زراعی, 1397; 7(Issue 1, pp. 1-89, Serial No. 22): 39-51. doi: 10.30473/icc.2018.4932

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

Iranian chemical communication
مقاله 4، دوره 7، Issue 1, pp. 1-89, Serial No. 22، فروردین 2019، صفحه 39-51    اصل مقاله (830.87 K)
نوع مقاله: Original Research Article
شناسه دیجیتال (DOI): 10.30473/icc.2018.4932
نویسندگان
Rashid Heidarimoghadam1؛ Seyede Shima Mortazavi2؛ Abbas Farmany* 3
1Department of Ergonomics, Health Sciences Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, IRAN.
2Young Researchers & Elite Club, Hamedan Branch, Islamic Azad University, Hamedan, IRAN
3Dental Research Center, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, IRAN
چکیده
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.
کلیدواژه‌ها
P. falciparum malaria؛ antimalarial compounds؛ 2,5-diaminobenzophenones؛ QSAR
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