New similarity measures for ligand-based virtual screening

The process of drug discovery using virtual screening techniques relies on “molecular similarity principle” which states that structurally similar molecules tend to have similar physicochemical and biological properties in comparison to other dissimilar molecules. Most of the existing virtual screen...

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Main Author:	Himmat, Mubarak Hussein Ibrahim
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/81800/1/MubarakHusseinIbrahimPFC2017.pdf
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id	my-utm-ep.81800
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spelling	my-utm-ep.818002019-09-24T09:36:53Z New similarity measures for ligand-based virtual screening 2017 Himmat, Mubarak Hussein Ibrahim QA75 Electronic computers. Computer science The process of drug discovery using virtual screening techniques relies on “molecular similarity principle” which states that structurally similar molecules tend to have similar physicochemical and biological properties in comparison to other dissimilar molecules. Most of the existing virtual screening methods use similarity measures such as the standard Tanimoto coefficient. However, these conventional similarity measures are inadequate, and their results are not satisfactory to researchers. This research investigated new similarity measures. It developed a novel similarity measure and molecules ranking method to retrieve molecules more efficiently. Firstly, a new similarity measure was derived from existing similarity measures, besides focusing on preferred similarity concepts. Secondly, new similarity measures were developed by reweighting some bit-strings, where features present in the compared molecules, and features not present in both compared molecules were given strong consideration. The final approach investigated ranking methods to develop a substitutional ranking method. The study compared the similarity measures and ranking methods with benchmark coefficients such as Tanimoto, Cosine, Dice, and Simple Matching (SM). The approaches were tested using standard data sets such as MDL Drug Data Report (MDDR), Directory of Useful Decoys (DUD) and Maximum Unbiased Validation (MUV). The overall results of this research showed that the new similarity measures and ranking methods outperformed the conventional industry- standard Tanimoto-based similarity search approach. The similarity measures are thus likely to support lead optimization and lead identification process better than methods based on Tanimoto coefficients. 2017 Thesis http://eprints.utm.my/id/eprint/81800/ http://eprints.utm.my/id/eprint/81800/1/MubarakHusseinIbrahimPFC2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:126072 phd doctoral Universiti Teknologi Malaysia Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Himmat, Mubarak Hussein Ibrahim New similarity measures for ligand-based virtual screening
description	The process of drug discovery using virtual screening techniques relies on “molecular similarity principle” which states that structurally similar molecules tend to have similar physicochemical and biological properties in comparison to other dissimilar molecules. Most of the existing virtual screening methods use similarity measures such as the standard Tanimoto coefficient. However, these conventional similarity measures are inadequate, and their results are not satisfactory to researchers. This research investigated new similarity measures. It developed a novel similarity measure and molecules ranking method to retrieve molecules more efficiently. Firstly, a new similarity measure was derived from existing similarity measures, besides focusing on preferred similarity concepts. Secondly, new similarity measures were developed by reweighting some bit-strings, where features present in the compared molecules, and features not present in both compared molecules were given strong consideration. The final approach investigated ranking methods to develop a substitutional ranking method. The study compared the similarity measures and ranking methods with benchmark coefficients such as Tanimoto, Cosine, Dice, and Simple Matching (SM). The approaches were tested using standard data sets such as MDL Drug Data Report (MDDR), Directory of Useful Decoys (DUD) and Maximum Unbiased Validation (MUV). The overall results of this research showed that the new similarity measures and ranking methods outperformed the conventional industry- standard Tanimoto-based similarity search approach. The similarity measures are thus likely to support lead optimization and lead identification process better than methods based on Tanimoto coefficients.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Himmat, Mubarak Hussein Ibrahim
author_facet	Himmat, Mubarak Hussein Ibrahim
author_sort	Himmat, Mubarak Hussein Ibrahim
title	New similarity measures for ligand-based virtual screening
title_short	New similarity measures for ligand-based virtual screening
title_full	New similarity measures for ligand-based virtual screening
title_fullStr	New similarity measures for ligand-based virtual screening
title_full_unstemmed	New similarity measures for ligand-based virtual screening
title_sort	new similarity measures for ligand-based virtual screening
granting_institution	Universiti Teknologi Malaysia
granting_department	Computing
publishDate	2017
url	http://eprints.utm.my/id/eprint/81800/1/MubarakHusseinIbrahimPFC2017.pdf
_version_	1747818417209999360

New similarity measures for ligand-based virtual screening

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