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Similarity-based virtual screening using bayesian inference network

Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do...

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Main Authors: Abdo, Ammar, Salim, Naomie
Format: Article
Published: Chemistry Central 2009
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/13101/
http://dx.doi.org/10.1186/1752-153X-3-S1-P44
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spelling my.utm.131012017-02-15T01:04:21Z http://eprints.utm.my/id/eprint/13101/ Similarity-based virtual screening using bayesian inference network Abdo, Ammar Salim, Naomie QA75 Electronic computers. Computer science Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches. Chemistry Central 2009 Article PeerReviewed Abdo, Ammar and Salim, Naomie (2009) Similarity-based virtual screening using bayesian inference network. Chemistry Central Journal, 3 . ISSN 1752-153X http://dx.doi.org/10.1186/1752-153X-3-S1-P44 doi:10.1186/1752-153X-3-S1-P44
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Abdo, Ammar
Salim, Naomie
Similarity-based virtual screening using bayesian inference network
description Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.
format Article
author Abdo, Ammar
Salim, Naomie
author_facet Abdo, Ammar
Salim, Naomie
author_sort Abdo, Ammar
title Similarity-based virtual screening using bayesian inference network
title_short Similarity-based virtual screening using bayesian inference network
title_full Similarity-based virtual screening using bayesian inference network
title_fullStr Similarity-based virtual screening using bayesian inference network
title_full_unstemmed Similarity-based virtual screening using bayesian inference network
title_sort similarity-based virtual screening using bayesian inference network
publisher Chemistry Central
publishDate 2009
url http://eprints.utm.my/id/eprint/13101/
http://dx.doi.org/10.1186/1752-153X-3-S1-P44
_version_ 1643646118304677888
score 13.211869

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