Similarity-based virtual screening with a 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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| Format: | Article |
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Wiley-VCH
2008
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| Online Access: | http://eprints.utm.my/8604/ |
| _version_ | 1848891725020397568 |
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| author | Abdo, Ammar Salim, Naomie |
| author_facet | Abdo, Ammar Salim, Naomie |
| author_sort | Abdo, Ammar |
| building | UTeM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T21:02:31Z |
| format | Article |
| id | utm-8604 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T21:02:31Z |
| publishDate | 2008 |
| publisher | Wiley-VCH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-86042009-05-06T04:35:14Z http://eprints.utm.my/8604/ Similarity-based virtual screening with a 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. Wiley-VCH 2008 Article PeerReviewed Abdo, Ammar and Salim, Naomie (2008) Similarity-based virtual screening with a bayesian inference network. ChemMedChem, 3 . pp. 1-10. ISSN 1860-7179 (Print) 1860-7187 (Electronic) http://www.ncbi.nlm.nih.gov/pubmed/19072820 |
| spellingShingle | QA75 Electronic computers. Computer science Abdo, Ammar Salim, Naomie Similarity-based virtual screening with a bayesian inference network |
| title | Similarity-based virtual screening with a bayesian inference network |
| title_full | Similarity-based virtual screening with a bayesian inference network |
| title_fullStr | Similarity-based virtual screening with a bayesian inference network |
| title_full_unstemmed | Similarity-based virtual screening with a bayesian inference network |
| title_short | Similarity-based virtual screening with a bayesian inference network |
| title_sort | similarity-based virtual screening with a bayesian inference network |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/8604/ http://eprints.utm.my/8604/ |