title: In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method.
abstract: We tested four machine learning methods, support vector machine (SVM), k-nearest neighbor, back-propagation neural network and C4.5 decision tree for their capability in predicting spleen tyrosine kinase (Syk) inhibitors by using 2592 compounds which are more diverse than those in other studies. The recursive feature elimination method was used for improving prediction performance and selecting molecular descriptors responsible for distinguishing Syk inhibitors and non-inhibitors. Among four machine learning models, SVM produces the best performance at 99.18% for inhibitors and 98.82% for non-inhibitors, respectively, indicating that the SVM is potentially useful for facilitating the discovery of Syk inhibitors.
authors: College of Chemistry, Key Laboratory of Green Chemistry and Technology in Ministry of Education, Sichuan University, Chengdu 610064, PR China.
authors: Bing-Ke
authors: BK
authors: Li
authors: Yong
authors: Y
authors: Cong
authors: Xue-Gang
authors: XG
authors: Yang
authors: Ying
authors: Y
authors: Xue
authors: Yi-Zong
authors: YZ
authors: Chen
mesh_terms: D000465:Algorithms; D001172:Arthritis, Rheumatoid / Q000378:metabolism; D003198:Computer Simulation; D003663:Decision Trees; D006801:Humans; D047908:Intracellular Signaling Peptides and Proteins / Q000037:antagonists & inhibitors*; D015233:Models, Statistical; D047428:Protein Kinase Inhibitors / Q000494:pharmacology*; D011505:Protein-Tyrosine Kinases / Q000037:antagonists & inhibitors*; D015203:Reproducibility of Results; D013154:Spleen / Q000187:drug effects / Q000201:enzymology*; D060388:Support Vector Machine; D000072377:Syk Kinase
chemical_list: D047908:Intracellular Signaling Peptides and Proteins; D047428:Protein Kinase Inhibitors; D011505:Protein-Tyrosine Kinases; C000606896:SYK protein, human; D000072377:Syk Kinase