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Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

机译:通过化学信息学方法探索G蛋白偶联受体(GPCR)配体空间:对合理药物设计的影响

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摘要

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the “golden age for GPCR structural biology.” Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand– and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
机译:合理药物发现的主要目标是鉴定作用于单个或多个药物靶标的选择性配体,以通过探索总化学空间来实现所需的临床结果。为了鉴定此类所需化合物,在预测其类药物性质时需要采用计算方法。 G蛋白偶联受体(GPCR)代表最大和最重要的整合膜蛋白家族之一。这些受体由于其在治疗各种疾病(如炎症性疾病,代谢失衡,心脏疾病,癌症,单基因疾病等)中的相关性而成为越来越有吸引力的药物靶标。在过去的十年中,许多三维(3D)解决了各种GPCR的结构,因此将这一时期称为“ GPCR结构生物学的黄金时代”。此外,有关GPCR配体化学性质的数据积累已引起人们对GPCR化学空间探索的极大兴趣。由于GPCR的结构,配体和功能数据稳定增长,因此在其药物开发流程中已实施了几种化学信息学方法。在这篇综述中,我们主要关注GPCR药物发现中基于化学信息学的范例。我们提供了基于配体和结构的化学信息学方法的综合观点,最好通过GPCR案例研究来说明。此外,还讨论了基于配体的知识与基于结构的知识的适当组合,即集成方法,该方法正成为基于化学信息学的GPCR药物设计的一种有前途的策略。

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