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The Scientific World Journal
Volume 2012, Article ID 410914, 7 pages
Research Article

Ligand-Based Virtual Screening Using Bayesian Inference Network and Reweighted Fragments

1Faculty of Computer Science and Information Systems, Universiti Tecknologi Malaysia, 81310 Skudai, Malaysia
2Faculty of Engineering, Karary University, Khartoum 12304, Sudan
3Department of Computer Science, Hodeidah University, Hodeidah, Yemen

Received 28 October 2011; Accepted 11 December 2011

Academic Editors: M. A. Fischl and G. D. Morse

Copyright © 2012 Ali Ahmed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.