a systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data从化学系统预测多种药物的相互作用,基因和药理数据.pdfVIP

A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data 1. 2. 1 3 2 1 1 1 Hua Yu , Jianxin Chen , Xue Xu , Yan Li , Huihui Zhao , Yupeng Fang , Xiuxiu Li , Wei Zhou , Wei Wang2*, Yonghua Wang1,4* 1 Bioinformatics Center, College of Life Sciences, Northwest AF University, Yangling, Shaanxi, China, 2 Beijing University of Chinese Medicine, ChaoYang District, Beijing, China, 3 Department of Chemical Engineering and Materials Science, Dalian University of Technology, Dalian, China, 4 State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest AF University, Yangling, Shaanxi, China Abstract In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF) and Support Vector Machine (SVM). The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematica