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

被引:11
作者
He, Huiqin [1 ]
Liu, Benquan [1 ]
Luo, Hongyi [1 ]
Zhang, Tingting [1 ]
Jiang, Jingwei [2 ]
机构
[1] China Pharmaceut Univ, Jiangsu Key Lab Drug Screening, Nanjing, Peoples R China
[2] China Pharmaceut Univ, Inst Pharmacol Sci, Nanjing, Peoples R China
关键词
big data; artificial intelligence; novel drugs; 3D structure; undruggable targets; hidden allosteric sites; HIDDEN ALLOSTERIC SITES; MOLECULAR DOCKING; BINDING-SITES; I-TASSER; SERVER; DEGRADATION; PREDICTION;
D O I
10.1136/svn-2019-000323
中图分类号
R74 [神经病学与精神病学];
学科分类号
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/cryogenic electron 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.
引用
收藏
页码:381 / 387
页数:7
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