Kawai Kentaro, Fujishima Satoshi, Takahashi Yoshimasa
Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Hibarigaoka 1-1, Tempaku-cho, Toyohashi 441-8580, Japan.
J Chem Inf Model. 2008 Jun;48(6):1152-60. doi: 10.1021/ci7004753. Epub 2008 Jun 6.
Aiming at the prediction of pleiotropic effects of drugs, we have investigated the multilabel classification of drugs that have one or more of 100 different kinds of activity labels. Structural feature representation of each drug molecule was based on the topological fragment spectra method, which was proposed in our previous work. Support vector machine (SVM) was used for the classification and the prediction of their activity classes. Multilabel classification was carried out by a set of the SVM classifiers. The collective SVM classifiers were trained with a training set of 59,180 compounds and validated by another set (validation set) of 29,590 compounds. For a test set that consists of 9,864 compounds, the classifiers correctly classified 80.8% of the drugs into their own active classes. The SVM classifiers also successfully performed predictions of the activity spectra for multilabel compounds.
针对药物多效性效应的预测，我们研究了具有100种不同活性标签中一种或多种的药物的多标签分类。每个药物分子的结构特征表示基于我们之前工作中提出的拓扑片段光谱法。支持向量机（SVM）用于分类及其活性类别的预测。多标签分类由一组SVM分类器进行。集体SVM分类器使用59180种化合物的训练集进行训练，并通过另一组29590种化合物（验证集）进行验证。对于由9864种化合物组成的测试集，分类器将80.8%的药物正确分类到其自身的活性类别中。SVM分类器还成功地对多标签化合物的活性光谱进行了预测。
