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基于量子化学描述符的自适应神经模糊推理系统在类胡萝卜素自由基清除活性定量构效关系研究中的应用

Adaptive Neuro-Fuzzy Inference System Applied QSAR with Quantum Chemical Descriptors for Predicting Radical Scavenging Activities of Carotenoids.

作者信息

Jhin Changho, Hwang Keum Taek

机构信息

Department of Food and Nutrition, Research Institute of Human Ecology, Seoul National University, Seoul, Korea.

出版信息

PLoS One. 2015 Oct 16;10(10):e0140154. doi: 10.1371/journal.pone.0140154. eCollection 2015.

Abstract

One of the physiological characteristics of carotenoids is their radical scavenging activity. In this study, the relationship between radical scavenging activities and quantum chemical descriptors of carotenoids was determined. Adaptive neuro-fuzzy inference system (ANFIS) applied quantitative structure-activity relationship models (QSAR) were also developed for predicting and comparing radical scavenging activities of carotenoids. Semi-empirical PM6 and PM7 quantum chemical calculations were done by MOPAC. Ionisation energies of neutral and monovalent cationic carotenoids and the product of chemical potentials of neutral and monovalent cationic carotenoids were significantly correlated with the radical scavenging activities, and consequently these descriptors were used as independent variables for the QSAR study. The ANFIS applied QSAR models were developed with two triangular-shaped input membership functions made for each of the independent variables and optimised by a backpropagation method. High prediction efficiencies were achieved by the ANFIS applied QSAR. The R-square values of the developed QSAR models with the variables calculated by PM6 and PM7 methods were 0.921 and 0.902, respectively. The results of this study demonstrated reliabilities of the selected quantum chemical descriptors and the significance of QSAR models.

摘要

类胡萝卜素的生理特性之一是其自由基清除活性。在本研究中,确定了类胡萝卜素的自由基清除活性与量子化学描述符之间的关系。还开发了应用定量构效关系模型(QSAR)的自适应神经模糊推理系统(ANFIS),用于预测和比较类胡萝卜素的自由基清除活性。通过MOPAC进行半经验PM6和PM7量子化学计算。中性和单价阳离子类胡萝卜素的电离能以及中性和单价阳离子类胡萝卜素的化学势乘积与自由基清除活性显著相关,因此这些描述符被用作QSAR研究的自变量。应用ANFIS的QSAR模型是通过为每个自变量制作两个三角形输入隶属函数并采用反向传播方法进行优化而开发的。应用ANFIS的QSAR实现了较高的预测效率。用PM6和PM7方法计算变量所建立的QSAR模型的R平方值分别为0.921和0.902。本研究结果证明了所选量子化学描述符的可靠性以及QSAR模型的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8048/4608816/fc6b943d3714/pone.0140154.g002.jpg

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