![]() |
个人信息Personal Information
教授
硕士生导师
性别:男
毕业院校:大连理工大学
学位:硕士
所在单位:化工学院
电子邮箱:zswei@dlut.edu.cn
A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest
点击次数:
论文类型:期刊论文
发表时间:2011-02-01
发表刊物:INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
收录刊物:Scopus、SCIE、PubMed
卷号:12
期号:2
页面范围:1259-1280
ISSN号:1422-0067
关键字:RSV; variable selection; Mold(2) descriptors; random forest
摘要:Experimental pEC(50)s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.