Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P.O. Box 14155-6455, Tehran, Iran.
Eur J Med Chem. 2009 Dec;44(12):5023-8. doi: 10.1016/j.ejmech.2009.09.006. Epub 2009 Sep 12.
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.
支持向量机（SVM）是机器学习领域的一种新算法，被用于开发 BK 通道激活剂的定量构效关系（QSAR）。数据集分为 57 个训练分子和 14 个测试分子。计算了大量描述符，并使用遗传算法（GA）选择了变量，以获得最佳拟合模型。将使用 SVM 获得的结果与多元线性回归（MLR）的结果进行比较，结果表明 SVM 模型明显优于 MLR 模型。改进的原因是化合物的活性与所选描述符呈非线性相关。此外，氧原子和氯原子之间的距离、分子的质量、范德华体积、电负性和极化率是影响研究化合物 BK 通道活性的主要独立因素。
