Frontiers in Computational Chemistry

Frontiers in Computational Chemistry

Volume: 4

Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug ...
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Computer-aided Drug Discovery Methodologies in the Modeling of Dual Target Ligands as Potential Parkinson’s Disease Therapeutics

Pp. 48-90 (43)

DOI: 10.2174/9781681084411118040004

Author(s): Yunierkis Perez-Castillo, Stellamaris Sotomayor-Burneo, Aliuska Morales Helguera, M. Natália D. S. Cordeiro, Eduardo Tejera, Cesar Pazy- Mino, Aminael Sanchez-Rodriguez, María F. Moreno, Marta Teijeira- Bautista, Evys Ancede-Gallardo, Fernanda Borges, Maykel Cruz- Monteagudo

Abstract

In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson´s disease (PD), the current hitting-one-target therapeutic strategy has proved inefficient. As a result, finding multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAOB), as well as adenosine A2A receptor (A2AAR) blockade, as a promising therapeutic approach to prevent neuronal cell death involved in PD. At the same time, computeraided drug discovery methodologies have become essential components of the drug discovery pipeline and Virtual Screening (VS) methodologies have emerged asefficient alternatives for the discovery of new drug candidates. In this chapter are summarized recent advances in the application of computer-aided drug discovery methodologies in the modeling of dual target, A2AAR and MAO-B, ligands for the discovery of potential PD’s therapeutics. The problem of finding potential dual target ligands of PD is addressed from different points of view: Similarity analyses, QSARderived ligand-based virtual screening studies and Molecular docking approaches. For the three types of drug design approaches the utility of ensemble methods in computeraided drug discovery is discussed.