Virtual screening tailored ensembles of QSAR models for the discovery of dual A_2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors

Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. Frequently no analysis is performed of the diversity of the ensemble members or no considerations regarding the applicability domain of the base model are made. In this research we propose a method employing genetic algorithms optimization for the generation of virtual screening tailored ensembles that address problems in the current applications of ensemble methods to virtual screening. The proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson’s disease therapeutics.