@article {10.3844/ajassp.2011.368.373,
article_type = {journal},
title = {An Enhancement of Bayesian Inference Network for Ligand-Based Virtual Screening using Features Selection},
author = {Ahmed, Ali and Abdo, Ammar and Salim, Naomie},
volume = {8},
year = {2011},
month = {Apr},
pages = {368-373},
doi = {10.3844/ajassp.2011.368.373},
url = {https://thescipub.com/abstract/ajassp.2011.368.373},
abstract = {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.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}