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

Ginny Y. Wong; Hung Fat Frank Leung; Steve S.H. Ling

doi:10.1109/IECON.2016.7793821

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

Ginny Y. Wong, Hung Fat Frank Leung, Steve S.H. Ling

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 Citations (Scopus)

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%.

Original language	English
Title of host publication	Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society
Publisher	IEEE Computer Society
Pages	939-944
Number of pages	6
ISBN (Electronic)	9781509034741
DOIs	https://doi.org/10.1109/IECON.2016.7793821
Publication status	Published - 21 Dec 2016
Event	42nd Conference of the Industrial Electronics Society, IECON 2016 - Palazzo dei Congressi, Florence, Italy Duration: 24 Oct 2016 → 27 Oct 2016

Conference

Conference	42nd Conference of the Industrial Electronics Society, IECON 2016
Country/Territory	Italy
City	Florence
Period	24/10/16 → 27/10/16

Keywords

Multi-clustering
Protein-ligand binding site
SVM

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

More information

10.1109/IECON.2016.7793821

http://hdl.handle.net/10397/107252

Cite this

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title = "Identification of protein-ligand binding site using multi-clustering and support vector machine",

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%.",

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author = "Wong, {Ginny Y.} and Leung, {Hung Fat Frank} and Ling, {Steve S.H.}",

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doi = "10.1109/IECON.2016.7793821",

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Wong, GY, Leung, HFF & Ling, SSH 2016, Identification of protein-ligand binding site using multi-clustering and support vector machine. in Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society., 7793821, IEEE Computer Society, pp. 939-944, 42nd Conference of the Industrial Electronics Society, IECON 2016, Florence, Italy, 24/10/16. https://doi.org/10.1109/IECON.2016.7793821

Identification of protein-ligand binding site using multi-clustering and support vector machine. / Wong, Ginny Y.; Leung, Hung Fat Frank; Ling, Steve S.H.
Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society. IEEE Computer Society, 2016. p. 939-944 7793821.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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T1 - Identification of protein-ligand binding site using multi-clustering and support vector machine

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AU - Leung, Hung Fat Frank

AU - Ling, Steve S.H.

PY - 2016/12/21

Y1 - 2016/12/21

N2 - 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%.

AB - 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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KW - SVM

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BT - Proceedings of the IECON 2016 - 42nd Annual Conference of the Industrial Electronics Society

PB - IEEE Computer Society

T2 - 42nd Conference of the Industrial Electronics Society, IECON 2016

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

More information

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