An Enhancement of Bayesian Inference Network for Ligand-Based Virtual Screening using Features Selection
- 1 ,
Copyright: © 2020 Ali Ahmed, Ammar Abdo and Naomie Salim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
- 1,531 Views
- 1,700 Downloads
- 1 Citations
- Features selection
- fingerprint features
- similarity search
- virtual screening
- Drug Data
- Bayesian Inference Network (BIN)
- proposed method
- High-Throughput Screening (HTS)
- Quantitative Structure-Activity Relationships (QSAR)