An Enhancement of Bayesian Inference Network for Ligand-Based Virtual Screening using Features Selection
Ali Ahmed · American Journal of Applied Sciences · 2011
Problem statement: 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.