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定量构效关系作为设计新型腺苷受体配体的有用工具。1. 激动剂。

Quantitative structure activity relationships as useful tools for the design of new adenosine receptor ligands. 1. Agonist.

作者信息

González Maykel Pérez, Terán Carmen, Teijeira Marta, Helguera Aliuska Morales

机构信息

Service Unit, Experimental Sugar Cane Station, Villa Clara, Cuba.

出版信息

Curr Med Chem. 2006;13(19):2253-66. doi: 10.2174/092986706777935195.

Abstract

In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.

摘要

为了尽量减少昂贵的药物研发失败,尽早确定新候选药物的潜在生物活性至关重要。鉴于目前有机合成中正在处理的大量核苷类似物文库,甚至在合成之前确定药物的生物活性是可取的,这可以通过预测生物活性方法来实现。从这个意义上说,诸如定量构效关系(QSAR)或对接方法等计算机辅助合理药物设计策略已成为有前途的工具。尽管文献中已经描述了大量用于预测不同生物活性的计算机模拟方法,但在开发对腺苷受体具有亲和力的新型激动剂分子中使用传统QSAR应用的情况却很少。本综述试图总结基于激动剂配体知识的QSAR模型对腺苷受体进行计算亲和力预测的当前知识水平。文献中已经描述了针对这些靶点的几种计算方案以及不同的二维和三维描述符,但该领域仍需要更多努力。

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