首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Quantitative structure-activity relationship study of serotonin (5-HT7) receptor inhibitors using modified ant colony algorithm and adaptive neuro-fuzzy interference system (ANFIS).
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Quantitative structure-activity relationship study of serotonin (5-HT7) receptor inhibitors using modified ant colony algorithm and adaptive neuro-fuzzy interference system (ANFIS).

机译:使用改进的蚁群算法和自适应神经模糊干扰系统(ANFIS)研究5-羟色胺(5-HT7)受体抑制剂的定量构效关系。

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摘要

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.
机译:采用修饰蚁群(ACO)方法和自适应神经模糊干扰系统(ANFIS),采用定量构效关系(QSAR)方法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。 )与改组交叉验证技术相结合。修改后的ACO算法用于选择QSAR建模中最重要的变量,然后将这些变量用作ANFIS的输入,以预测喹唑啉酮衍生物的5-HT(7)受体结合活性。描述抑制机理的最佳描述子是Q(max),Se,Hy,PJI3和DELS,它们是电子,结构,几何和经验描述子。 R(2)和均方根误差的统计参数分别为0.775和0.360。修饰的ACO-ANFIS模型预测喹唑啉酮衍生物(pIC(50))抑制行为的能力和鲁棒性通过留一法和留多法交叉验证的验​​证技术以及Y随机化技术进行了说明。还研究了改进的ACO-ANFIS方法与其他两种方法的比较,即逐步MLR-ANFIS和GA-PLS-ANFIS,结果表明,该模型在这项工作中优于其他方法。

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