Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11053
Title: Predicting protein-ligand binding site with support vector machine
Authors: Wong, GY
Leung, FHF 
Keywords: Pharmaceutical industry
Proteins
Support vector machines
Issue Date: 2010
Publisher: IEEE
Source: 2010 IEEE Congress on Evolutionary Computation (CEC), 18-23 July 2010, Barcelona, p. 1-5 How to cite?
Abstract: Identification of protein-ligand binding site is an important task in structure-based drug design and docking algorithms. In these two decades, many different approaches have been developed to predict the binding site, such as geometric, energetic and sequence-based methods. We present the binding site prediction algorithm that takes advantage of both sequence conservation and geometric methods for pocket finding (LIGSITE and SURFNET). SVM is used to cluster the pockets, which are most likely to bind ligands with the attributes of grid value, interaction potential and offset from protein. We compare our algorithm to four other approaches: LIGSITE, SURFNET, PocketFinder and Concavity. Our algorithm is found to provide the highest success rate.
URI: http://hdl.handle.net/10397/11053
ISBN: 978-1-4244-6909-3
DOI: 10.1109/CEC.2010.5586110
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

2
Last Week
2
Last month
Citations as of Sep 17, 2017

Page view(s)

32
Last Week
1
Last month
Checked on Sep 18, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.