Institute of Applied Chemistry, China West Normal University, Nanchong 637002, Sichuan, China.
Int J Mol Sci. 2009 Jul 29;10(8):3316-3337. doi: 10.3390/ijms10083316.
In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-MLR) methods have been used to search descriptor space and select the descriptors which are responsible for the inhibitory activity of these compounds. The model containing seven descriptors found by Adaboost-SVM, has showed better predictive capability than the other models. The total accuracy in prediction for the training and test set is 100.0% and 95.0% for PSO-Adaboost-SVM, 99.1% and 92.5% for PSO-SVM, 99.1% and 82.5% for Stepwise-MLR-Adaboost-SVM, 99.1% and 77.5% for Stepwise-MLR-SVM, respectively. The results indicate that Adaboost-SVM can be used as a useful modeling tool for QSAR studies.
在本工作中，支持向量机（SVM）和自适应增强支持向量机（Adaboost-SVM）已被用于开发一种分类模型，作为一种潜在的新型 5-HT(1A) 选择性配体的筛选机制。每个化合物都由计算结构描述符表示，这些描述符编码拓扑特征。粒子群优化（PSO）和逐步多元线性回归（Stepwise-MLR）方法已被用于搜索描述符空间并选择负责这些化合物抑制活性的描述符。Adaboost-SVM 发现的包含七个描述符的模型显示出比其他模型更好的预测能力。对于训练集和测试集，PSO-Adaboost-SVM 的总准确率分别为 100.0%和 95.0%，PSO-SVM 的总准确率分别为 99.1%和 92.5%，Stepwise-MLR-Adaboost-SVM 的总准确率分别为 99.1%和 82.5%，Stepwise-MLR-SVM 的总准确率分别为 99.1%和 77.5%。结果表明，Adaboost-SVM 可作为 QSAR 研究的有用建模工具。
