Identification of protein-ligand binding site using multi-clustering and support vector machine

Wong, GY; Leung, FHF; Ling, SSH

Identification of protein-ligand binding site using multi-clustering and support vector machine

Wong, GY Leung, FHF Ling, SSH

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Publication Type:: Conference Proceeding
Citation:: IECON Proceedings (Industrial Electronics Conference), 2016, pp. 939 - 944
Issue Date:: 2016-12-21

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Field	Value	Language
dc.contributor.author	Wong, GY	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Ling, SSH https://orcid.org/0000-0003-0849-5098	en_US
dc.date.issued	2016-12-21	en_US
dc.identifier.citation	IECON Proceedings (Industrial Electronics Conference), 2016, pp. 939 - 944	en_US
dc.identifier.isbn	9781509034741	en_US
dc.identifier.uri	http://hdl.handle.net/10453/80681
dc.description.abstract	© 2016 IEEE. 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%.	en_US
dc.relation.ispartof	IECON Proceedings (Industrial Electronics Conference)	en_US
dc.relation.isbasedon	10.1109/IECON.2016.7793821	en_US
dc.title	Identification of protein-ligand binding site using multi-clustering and support vector machine	en_US
dc.type	Conference Proceeding
utslib.for	090302 Biomechanical Engineering	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. 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%.

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http://hdl.handle.net/10453/80681