Ghanbari Zahra, Housaindokht Mohammad R, Izadyar Mohammad, Bozorgmehr Mohammad R, Eshtiagh-Hosseini Hossein, Bahrami Ahmad R, Matin Maryam M, Khoshkholgh Maliheh Javan
Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran.
Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran.
ScientificWorldJournal. 2014;2014:745649. doi: 10.1155/2014/745649. Epub 2014 Apr 6.
Quantitative structure activity relationship (QSAR) for the anticancer activity of Fe(III)-salen and salen-like complexes was studied. The methods of density function theory (B3LYP/LANL2DZ) were used to optimize the structures. A pool of descriptors was calculated: 1497 theoretical descriptors and quantum-chemical parameters, shielding NMR, and electronic descriptors. The study of structure and activity relationship was performed with multiple linear regression (MLR) and artificial neural network (ANN). In nonlinear method, the adaptive neuro-fuzzy inference system (ANFIS) was applied in order to choose the most effective descriptors. The ANN-ANFIS model with high statistical significance (R (2) train = 0.99, RMSE = 0.138, and Q (2) LOO = 0.82) has better capability to predict the anticancer activity of the new compounds series of this family. Based on this study, anticancer activity of this compound is mainly dependent on the geometrical parameters, position, and the nature of the substituent of salen ligand.
研究了Fe(III)-salen及类salen配合物抗癌活性的定量构效关系。采用密度泛函理论方法（B3LYP/LANL2DZ）优化结构。计算了一系列描述符：1497个理论描述符、量子化学参数、屏蔽核磁共振及电子描述符。运用多元线性回归（MLR）和人工神经网络（ANN）进行构效关系研究。在非线性方法中，应用自适应神经模糊推理系统（ANFIS）以选择最有效的描述符。具有高统计显著性的ANN-ANFIS模型（训练集R(2)=0.99，均方根误差RMSE = 0.138，留一法交叉验证Q(2)LOO = 0.82）对该家族新化合物系列的抗癌活性具有更好的预测能力。基于该研究，该化合物的抗癌活性主要取决于salen配体的几何参数、取代基的位置及性质。
