Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources
- PMID: 33117147
- PMCID: PMC7553047
- DOI: 10.3389/fphar.2020.01221
Molecular Modeling Applied to the Discovery of New Lead Compounds for P2 Receptors Based on Natural Sources
Abstract
P2 receptors are a family of transmembrane receptors activated by nucleotides and nucleosides. Two classes have been described in mammals, P2X and P2Y, which are implicated in various diseases. Currently, only P2Y12 has medicines approved for clinical use as antiplatelet agents and natural products have emerged as a source of new drugs with action on P2 receptors due to the diversity of chemical structures. In drug discovery, in silico virtual screening (VS) techniques have become popular because they have numerous advantages, which include the evaluation of thousands of molecules against a target, usually proteins, faster and cheaper than classical high throughput screening (HTS). The number of studies using VS techniques has been growing in recent years and has led to the discovery of new molecules of natural origin with action on different P2X and P2Y receptors. Using different algorithms it is possible to obtain information on absorption, distribution, metabolism, toxicity, as well as predictions on biological activity and the lead-likeness of the selected hits. Selected biomolecules may then be tested by molecular dynamics and, if necessary, rationally designed or modified to improve their interaction for the target. The algorithms of these in silico tools are being improved to permit the precision development of new drugs and, in the future, this process will take the front of drug development against some central nervous system (CNS) disorders. Therefore, this review discusses the methodologies of in silico tools concerning P2 receptors, as well as future perspectives and discoveries, such as the employment of artificial intelligence in drug discovery.
Keywords: P2 receptors; drug discovery; homology modeling; molecular dynamics; molecular modelling; natural products; virtual screening.
Copyright © 2020 Alberto, da Silva Ferreira, Soares and Alves.
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