title: Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector machine.
abstract: Several QSAR (quantitative structure-activity relationships) models for predicting the inhibitory activity of 117 Aurora-A kinase inhibitors were developed. The whole dataset was split into a training set and a test set based on two different methods, (1) by a random selection; and (2) on the basis of a Kohonen's self-organizing map (SOM). Then the inhibitory activity of 117 Aurora-A kinase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) methods, respectively. For the two MLR models and the two SVM models, for the test sets, the correlation coefficients of over 0.92 were achieved.
authors.affiliation: 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. yanax@mail.buct.edu.cn
authors.forename: Aixia
authors.initials: A
authors.lastname: Yan
authors.forename: Yang
authors.initials: Y
authors.lastname: Chong
authors.forename: Liyu
authors.initials: L
authors.lastname: Wang
authors.forename: Xiaoying
authors.initials: X
authors.lastname: Hu
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
mesh_terms: D064127:Aurora Kinases; D047428:Protein Kinase Inhibitors / Q000737:chemistry* / Q000494:pharmacology; D017346:Protein Serine-Threonine Kinases / Q000037:antagonists & inhibitors* / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: D047428:Protein Kinase Inhibitors; D064127:Aurora Kinases; D017346:Protein Serine-Threonine Kinases