Background: 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. No analysis of the diversity of the ensemble members is performed frequently or no considerations regarding the applicability domain of the base models are being made.
Methods: In this research, we review basic concepts and definitions related to virtual screening. We comment recent applications of ensemble methods to ligand-based virtual screening and highlight their advantages and limitations.
Results: Next, we propose a method based on genetic algorithms optimization for the generation of virtual screening tailored ensembles which address the previously identified problems in the current applications of ensemble methods to virtual screening.
Conclusion: Finally, 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.