Ahmed, A. and Mohammed Hassan, Ammar Abdo and Salim, Naomie (2011) An enhancement of bayesian inference network for ligand-based virtual screening using features selection. American Journal of Applied Sciences, 8 (4). pp. 368-373. ISSN 1546-9239
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Official URL: http://dx.doi.org/10.3844/ajassp.2011.368.373
Similarity based Virtual Screening (VS) deals with a large amount of data containing irrelevant and/or redundant fragments or features. Recent use of Bayesian network as an alternative for existing tools for similarity based VS has received noticeable attention of the researchers in the field of chemoinformatics. Approach: To this end, different models of Bayesian network have been developed. In this study, we enhance the Bayesian Inference Network (BIN) using a subset of selected molecule's features. Results: In this approach, a few features were filtered from the molecular fingerprint features based on a features selection approach. Conclusion: Simulated virtual screening experiments with MDL Drug Data Report (MDDR) data sets showed that the proposed method provides simple ways of enhancing the cost effectiveness of ligand-based virtual screening searches, especially for higher diversity data set.
|Uncontrolled Keywords:||bayesian inference network (BIN), drug data, features selection, fingerprint features, high-throughput screening (HTS), proposed method, quantitative structure-activity relationships (QSAR), similarity search, virtual screening|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Liza Porijo|
|Deposited On:||16 Nov 2012 03:07|
|Last Modified:||13 Feb 2017 02:47|
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