Bayesian approaches for ligand-based virtual screening applications

Abstract

The objective of computer aided drug design is to discover new drugs by carrying out algorithmic modelling of chemical interactions of bioactive molecules. Drug discovery is known to be a notoriously lengthy and costly procedure, therefore this sparks a great motivation for further research in the field to be carried out in order to simultaneously reduce the time elapsed during drug discovery and also produce effective products. Virtual screening is an umbrella term for a variety of ligand-based and structure-based tools which are used to search databases of chemical structures. Of particular interest to this study is ligand-based virtual screening and this uses known and active compounds to a specific target to screen molecules of unknown activity. We explore the way in which statistical approaches, specifically Bayesian statistics have been adopted for Ligand-based Virtual Screening. We implement two main similarity models, the Bayesian Inference Network and the Bayesian Belief Network, as well as explore model tuning avenues as an attempt to improve upon our results. The first crucial research question we seek to answer is if such statistical approaches provide better screening results when compared to conventional similarity scoring techniques, specifically the Tanimoto similarity metric. Indeed, through our research we show that the Bayesian similarity models developed through this dissertation do in fact improve screening results. Significant improvements in the ROC AUC is recorded when the Bayesian Inference Network and the Bayesian Belief Network are employed instead of the Tanimoto similarity metric, with maximum improvements of 15.52% and 15.19% respectively. Secondly, we aim to determine whether screening effectiveness is improved when multiple actives to a known target are used to rank a compound database as opposed to a single active. Through this research we suggest that for such Bayesian similarity models for Ligand-based Virtual Screening, a single active provides better results.