An enhancement of Bayesian inference network for ligand-based virtual screening using minifingerprints

Ali Ahmed; Ammar Abdo; Naomie Salim

doi:10.1117/12.920338

13 January 2012 An enhancement of Bayesian inference network for ligand-based virtual screening using minifingerprints

Ali Ahmed, Ammar Abdo, Naomie Salim

Proceedings Volume 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies; 83502U (2012) https://doi.org/10.1117/12.920338
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore

Abstract

Selection and identification of a subset of compounds from libraries or databases, which are likely to possess a desired biological activity is the main target of ligand-based virtual screening approaches. The main challenge of such approaches is achieving of high recall of active molecules. 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. In this approach, a few features that represent the Minifingerprints (MFPs) were filtered from the molecular fingerprint features based on an analysis of distributions of molecular descriptors and structural fragments into large compound data set collections. 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.

Citation Download Citation

Ali Ahmed, Ammar Abdo, and Naomie Salim "An enhancement of Bayesian inference network for ligand-based virtual screening using minifingerprints", Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83502U (13 January 2012); https://doi.org/10.1117/12.920338

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