The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM.
Journal:
Computational and mathematical methods in medicine
Published Date:
Jan 1, 2016
Abstract
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.
Authors
Keywords
Algorithms
Area Under Curve
Binding Sites
Cathepsin K
Chemistry, Pharmaceutical
Combinatorial Chemistry Techniques
Computational Biology
Crystallization
Databases, Protein
Drug Design
Humans
Hydrogen
Imaging, Three-Dimensional
Ligands
Models, Statistical
Protein Binding
Protein Conformation
Proteins
Reproducibility of Results
ROC Curve
Software
Support Vector Machine