Yao Xiaojun, Liu Huanxiang, Zhang Ruisheng, Liu Mancang, Hu Zhide, Panaye A, Doucet J P, Fan Botao
Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
Mol Pharm. 2005 Sep-Oct;2(5):348-56. doi: 10.1021/mp050027v.
The least squares support vector machine (LSSVM), as a novel machine learning algorithm, was used to develop quantitative and classification models as a potential screening mechanism for a novel series of 1,4-dihydropyridine calcium channel antagonists for the first time. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modeling results in a nonlinear, seven-descriptor model based on LSSVM with mean-square errors 0.2593, a predicted correlation coefficient (R(2)) 0.8696, and a cross-validated correlation coefficient (R(cv)(2)) 0.8167. The best classification results are found using LSSVM: the percentage (%) of correct prediction based on leave one out cross-validation was 91.1%. This paper provides a new and effective method for drug design and screening.
最小二乘支持向量机（LSSVM）作为一种新型机器学习算法，首次被用于开发定量和分类模型，作为一系列新型1,4 - 二氢吡啶钙通道拮抗剂的潜在筛选机制。每种化合物由计算得到的结构描述符表示，这些描述符编码了组成、拓扑、几何、静电和量子化学特征。然后使用启发式方法搜索描述符空间并选择负责活性的描述符。基于LSSVM的定量建模得到一个非线性的七描述符模型，其均方误差为0.2593，预测相关系数（R(2)）为0.8696，交叉验证相关系数（R(cv)(2)）为0.8167。使用LSSVM发现了最佳分类结果：基于留一法交叉验证的正确预测百分比（%）为91.1%。本文为药物设计和筛选提供了一种新的有效方法。
