Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A2AAdenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors.

Abstract:

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.

journal_name

Curr Pharm Des

authors

Helguera AM,Perez-Castillo Y,D S Cordeiro MN,Tejera E,Paz-Y-Miño C,Sánchez-Rodríguez A,Teijeira M,Ancede-Gallardo E,Cagide F,Borges F,Cruz-Monteagudo M

doi

10.2174/1381612822666160302103542

subject

Has Abstract

pub_date

2016-01-01 00:00:00

pages

3082-96

issue

21

eissn

1381-6128

issn

1873-4286

pii

CPD-EPUB-74103

journal_volume

22

pub_type

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