Implementing relevance feedback in ligand-based virtual screening using bayesian inference network
Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-ba...
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| Format: | Article |
| Language: | English |
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Society for Laboratory Automation and Screening
2011
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| Online Access: | http://eprints.utm.my/29163/1/AmmarAbdo2011_ImplementingRelevanceFeedbackinLigand-BasedVirtualScreening.pdf |
| _version_ | 1796856502583033856 |
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| author | Abdo, Ammar Salim, Naomie Ahmed, Ali |
| author_facet | Abdo, Ammar Salim, Naomie Ahmed, Ali |
| author_sort | Abdo, Ammar |
| collection | ePrints |
| description | Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets. |
| first_indexed | 2024-03-05T18:43:55Z |
| format | Article |
| id | utm.eprints-29163 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:43:55Z |
| publishDate | 2011 |
| publisher | Society for Laboratory Automation and Screening |
| record_format | dspace |
| spelling | utm.eprints-291632019-03-17T03:03:13Z http://eprints.utm.my/29163/ Implementing relevance feedback in ligand-based virtual screening using bayesian inference network Abdo, Ammar Salim, Naomie Ahmed, Ali QA75 Electronic computers. Computer science Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets. Society for Laboratory Automation and Screening 2011-10 Article PeerReviewed application/pdf en http://eprints.utm.my/29163/1/AmmarAbdo2011_ImplementingRelevanceFeedbackinLigand-BasedVirtualScreening.pdf Abdo, Ammar and Salim, Naomie and Ahmed, Ali (2011) Implementing relevance feedback in ligand-based virtual screening using bayesian inference network. Journal of Biomolecular Screening, 16 (9). pp. 1081-1088. ISSN 1087-0571 http://dx.doi.org/10.1177/1087057111416658 DOI:10.1177/1087057111416658 |
| spellingShingle | QA75 Electronic computers. Computer science Abdo, Ammar Salim, Naomie Ahmed, Ali Implementing relevance feedback in ligand-based virtual screening using bayesian inference network |
| title | Implementing relevance feedback in ligand-based virtual screening using bayesian inference network |
| title_full | Implementing relevance feedback in ligand-based virtual screening using bayesian inference network |
| title_fullStr | Implementing relevance feedback in ligand-based virtual screening using bayesian inference network |
| title_full_unstemmed | Implementing relevance feedback in ligand-based virtual screening using bayesian inference network |
| title_short | Implementing relevance feedback in ligand-based virtual screening using bayesian inference network |
| title_sort | implementing relevance feedback in ligand based virtual screening using bayesian inference network |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/29163/1/AmmarAbdo2011_ImplementingRelevanceFeedbackinLigand-BasedVirtualScreening.pdf |
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