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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 4809831, 9 pages
Research Article

The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM

1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
2School of Software, Harbin University of Science and Technology, Harbin 150080, China

Received 27 December 2015; Revised 6 March 2016; Accepted 7 March 2016

Academic Editor: Ezequiel López-Rubio

Copyright © 2016 Meng-yu Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To solve this problem, we present a method using the ensemble learning to improve the support vector machine to process the generated protein-ligand interaction fingerprint (IFP). By combining multiple classifiers, ensemble learning is able to avoid the limitations of the single classifier’s performance and obtain better generalization. According to the research of virtual screening experiment with SRC and Cathepsin K as the target, the results show that the ensemble learning method can effectively reduce the error because the sample quality is not high and improve the effect of the whole virtual screening process.