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Virtual screening tailored ensembles of QSAR models for the discovery of dual A2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors
1 , 1, 2 , 3 , 4 , 4 , 5 , 6, 7 , 1 , 8 , * 8 , * 4, 8
1  Molecular Simulation and Drug Design Group, Centro de Bioactivos Químicos (CBQ), Central University of Las Villas, Santa Clara, 54830, Cuba
2  Sección Físico Química y Matemáticas, Departamento de Química, Universidad Técnica Particular de Loja, San Cayetano Alto S/N, EC1101608 Loja, Ecuador
3  REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
4  Instituto de Investigaciones Biomédicas (IIB), Universidad de Las Américas, 170513 Quito, Ecuador
5  Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador
6  Departamento de Química Orgánica. Facultade de Química, Universidade de Vigo, 36310 Vigo, Spain
7  Instituto de Investigación Biomédica (IVIB), Universidade de Vigo, 36310 Vigo, Spain
8  CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal.


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.

Keywords: Dual-target drugs, Virtual screening, MAO-B inhibitors, A2A adenosine receptors antagonist, Ensemble modeling, QSAR