In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method

Authors:
Bing-Ke Li;Yong Cong;Xue-Gang Yang;Ying Xue;Yi-Zong Chen
Affiliations:
College of Chemistry, Key Laboratory of Green Chemistry and Technology in Ministry of Education, Sichuan University, Chengdu 610064, PR China;College of Chemistry, Key Laboratory of Green Chemistry and Technology in Ministry of Education, Sichuan University, Chengdu 610064, PR China;College of Chemistry, Key Laboratory of Green Chemistry and Technology in Ministry of Education, Sichuan University, Chengdu 610064, PR China;College of Chemistry, Key Laboratory of Green Chemistry and Technology in Ministry of Education, Sichuan University, Chengdu 610064, PR China and Key Laboratory of Advanced Scientific Computation ...;Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543, Singapore
Venue:
Computers in Biology and Medicine
Year:
2013

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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.