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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity.
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Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity.

机译：遗传算法-支持向量机（GA-SVM）在预测BK通道活性中的应用。

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摘要

The support vector machine (SVM), which is a novel algorithm from the machine learning community, was used to develop quantitative structure-activity relationship (QSAR) for BK-channel activators. The data set was divided into 57 molecules of training and 14 molecules of test sets. A large number of descriptors were calculated and genetic algorithm (GA) was used to select variables that resulted in the best-fitted for models. A comparison between the obtained results using SVM with those of multi-parameter linear regression (MLR) revealed that SVM model was much better than MLR model. The improvements are due to the fact that the activity of the compounds demonstrates non-linear correlations with the selected descriptors. Also distances between Oxygen and Chlorine atoms, the mass, the van der Waals volume, the electronegativity, and the polarizability of the molecules are the main independent factors contributing to the BK-channels activity of the studied compounds.
机译：支持向量机（SVM）是机器学习社区的一种新颖算法，用于开发BK通道激活剂的定量构效关系（QSAR）。数据集分为57个分子训练和14个分子测试集。计算了大量的描述符，并使用遗传算法（GA）来选择最适合模型的变量。将使用SVM的结果与多参数线性回归（MLR）的结果进行比较，发现SVM模型比MLR模型要好得多。所述改进归因于以下事实：化合物的活性与所选描述符表现出非线性相关性。氧和氯原子之间的距离，分子的质量，范德华体积，电负性和极化性也是影响所研究化合物BK通道活性的主要独立因素。

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