Jalali-Heravi M, Asadollahi-Baboli M
Department of Chemistry, Sharif University of Technology, P.O. Box 11155-9516, Tehran, Iran.
Eur J Med Chem. 2009 Apr;44(4):1463-70. doi: 10.1016/j.ejmech.2008.09.050. Epub 2008 Oct 14.
Quantitative structure-activity relationship (QSAR) approach was carried out for the prediction of inhibitory activity of some novel quinazolinone derivatives on serotonin (5-HT(7)) using modified ant colony (ACO) method and adaptive neuro-fuzzy interference system (ANFIS) combined with shuffling cross-validation technique. A modified ACO algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict 5-HT(7) receptor binding activities of quinazolinone derivatives. The best descriptors describing the inhibition mechanism are Q(max), Se, Hy, PJI3 and DELS which are among electronic, constitutional, geometric and empirical descriptors. The statistical parameters of R(2) and root mean square error are 0.775 and 0.360, respectively. The ability and robustness of modified ACO-ANFIS model in predicting inhibition behavior of quinazolinone derivatives (pIC(50)) are illustrated by validation techniques of leave-one-out and leave-multiple-out cross-validations and also by Y-randomization technique. Comparison of the modified ACO-ANFIS method with two other methods, that is, stepwise MLR-ANFIS and GA-PLS-ANFIS were also studied and the results indicated that the proposed model in this work is superior over the others.
采用改进蚁群算法(ACO)和自适应神经模糊推理系统(ANFIS)结合洗牌交叉验证技术,开展了定量构效关系(QSAR)研究,以预测某些新型喹唑啉酮衍生物对5-羟色胺(5-HT(7))的抑制活性。利用改进的ACO算法在QSAR建模中选择最重要的变量,然后将这些变量作为ANFIS的输入,以预测喹唑啉酮衍生物的5-HT(7)受体结合活性。描述抑制机制的最佳描述符是Q(max)、Se、Hy、PJI3和DELS,它们属于电子、结构、几何和经验描述符。统计参数R(2)和均方根误差分别为0.775和0.360。通过留一法和留多法交叉验证以及Y随机化技术的验证,说明了改进的ACO-ANFIS模型预测喹唑啉酮衍生物抑制行为(pIC(50))的能力和稳健性。还研究了改进的ACO-ANFIS方法与另外两种方法(即逐步MLR-ANFIS和GA-PLS-ANFIS)的比较,结果表明本文提出的模型优于其他模型。