Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/107252
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Title: Identification of protein-ligand binding site using multi-clustering and Support Vector Machine
Authors: Wong, GY 
Leung, FHF 
Ling, SSH
Issue Date: 2016
Source: In Proceedings of IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 23-26 October 2016, Florence, Italy, p. 939-944
Abstract: Multi-clustering has been widely used. It acts as a pre-training process for identifying protein-ligand binding in structure-based drug design. Then, the Support Vector Machine (SVM) is employed to classify the sites most likely for binding ligands. Three types of attributes are used, namely geometry-based, energy-based, and sequence conservation. Comparison is made on 198 drug-target protein complexes with LIGSITECSC, SURFNET, Fpocket, Q-SiteFinder, ConCavity, and MetaPocket. The results show an improved success rate of up to 86%.
Keywords: Multi-clustering
Protein-ligand binding site
SVM
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-5090-3474-1 (Electronic)
978-1-5090-3475-8 (Print on Demand(PoD))
DOI: 10.1109/IECON.2016.7793821
Description: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 23-26 October 2016, Florence, Italy
Rights: ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication G. Y. Wong, F. H. F. Leung and S. S. H. Ling, "Identification of protein-ligand binding site using multi-clustering and Support Vector Machine," IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 2016, pp. 939-944 is available at https://doi.org/10.1109/IECON.2016.7793821.
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