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Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets

机译:大数据和人工智能探索靶向蛋白质的新药没有3D结构并克服不可驾命的目标

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摘要

The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures of some proteins (the so-called undruggable targets) are known, their targeted drugs are still absent. As increasing crystal/cryogenicelectron microscopy structures are deposited in Protein Data Bank, it is much more possible to discover the targeted drugs. Moreover, it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites. In this review, we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.
机译:靶向药物的发现严重依赖于靶蛋白的三维(3D)结构。当蛋白质目标的3D结构未知时,很难设计其相应的靶向药物。虽然某些蛋白质的3D结构(所谓的不可驾照靶标)是已知的,但它们的靶向药物仍然存在。随着晶体/低温的增加电子显微镜结构沉积在蛋白质数据库中,更有可能发现靶向药物。此外,当我们识别隐藏的颠振站时,它也非常可能将以前的不可驾驶的目标转化为可用的目标。在这篇综述中,我们专注于目前可用的先进方法,用于发现靶向蛋白质的新化合物,没有3D结构,以及如何将不可驾驶的目标转化为可用的毒品。

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