title: Prediction of bioactivity of HIV-1 integrase ST inhibitors by multilinear regression analysis and support vector machine.
abstract: In this study, four computational quantitative structure-activity relationship models were built to predict the biological activity of HIV-1 integrase strand transfer (ST) inhibitors. 551 Inhibitors whose bioactivities were detected by radiolabeling method were collected. The molecules were represented with 20 selected MOE descriptors. All inhibitors were divided into a training set and a test set with two methods: (1) by a Kohonen's self-organizing map (SOM); (2) by a random selection. For every training set and test set, a multilinear regression (MLR) analysis and a support vector machine (SVM) were used to establish models, respectively. For the test set divided by SOM, the correlation coefficients (rs) were over 0.91, and for the test set split randomly, the rs were over 0.86.
authors: State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, PO Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, PR China.
authors: Shouyi
authors: S
authors: Xuan
authors: Yanbin
authors: Y
authors: Wu
authors: Xiaofang
authors: X
authors: Chen
authors: Jun
authors: J
authors: Liu
authors: Aixia
authors: A
authors: Yan
mesh_terms: D019427:HIV Integrase / Q000737:chemistry* / Q000378:metabolism; D015497:HIV-1 / Q000201:enzymology*; D006801:Humans; D019429:Integrase Inhibitors / Q000737:chemistry* / Q000378:metabolism; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis; D060388:Support Vector Machine
chemical_list: D019429:Integrase Inhibitors; D019427:HIV Integrase; C514801:p31 integrase protein, Human immunodeficiency virus 1