RT Journal Article SR Electronic T1 Efficient repositioning of approved drugs as anti-HIV agents using machine learning based web server Anti-HIV-Predictor JF bioRxiv FD Cold Spring Harbor Laboratory SP 087445 DO 10.1101/087445 A1 Shao-Xing Dai A1 Huan Chen A1 Wen-Xing Li A1 Yi-Cheng Guo A1 Jia-Qian Liu A1 Jun-Juan Zheng A1 Qian Wang A1 Hui-Juan Li A1 Bi-Wen Chen A1 Yue-Dong Gao A1 Gong-Hua Li A1 Yong-Tang Zheng A1 Jing-Fei Huang YR 2017 UL http://biorxiv.org/content/early/2017/01/04/087445.abstract AB Treatment of AIDS still faces multiple challenges such as drug resistance and HIV eradication. Development of new, effective and affordable drugs against HIV is urgently needed. In this study, we developed a world’s first web server called Anti-HIV-Predictor (http://bsb.kiz.ac.cn:70/hivpre) for predicting anti-HIV activity of given compounds. This machine learning based web server is rapid and accurate (accuracy >93% and AUC > 0.958), which enables us to screen tens of millions of compounds and discover new anti-HIV agents. We firstly applied the server to screen 1835 approved drugs for anti-HIV therapy. Then the predicted new anti-HIV compounds were experimentally evaluated. Finally, we repurposed 7 approved drugs (cetrorelix, dalbavancin, daunorubicin, doxorubicin, epirubicin, idarubicin and valrubicin) as new anti-HIV agents. The original indication of these drugs is involved in a variety of diseases such as female infertility, acute bacterial infections, leukemia and other cancers. Anti-HIV-Predictor and the 7 repurposed anti-HIV agents provided here demonstrate the efficacy of this strategy for discovery of new anti-HIV agents. This strategy and the server should significantly advance current anti-HIV research.