Deep learning-driven scaffold hopping in the discovery of Akt kinase inhibitors

被引：6

作者：

Wang, Zuqin ^{[1
]}

Ran, Ting ^{[2
]}

Xu, Fang ^{[1
]}

Wen, Chang ^{[2
]}

Song, Shukai ^{[1
]}

Zhou, Yang ^{[1
]}

Chen, Hongming ^{[2
]}

Lu, Xiaoyun ^{[1
]}

机构：

[1] Jinan Univ, Coll Pharm, 601 Huangpu Ave West, Guangzhou 510632, Peoples R China

[2] Bioland Lab Guangzhou Regenerat Med & Hlth Guangd, Guangzhou 510530, Peoples R China

来源：

CHEMICAL COMMUNICATIONS | 2021年 / 57卷 / 81期

基金：

中国国家自然科学基金;

关键词：

PROGRAM; DESIGN; FINGERPRINTS; FRAGMENT; PATHWAY; CANCER;

D O I：

10.1039/d1cc03392a

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Scaffold hopping has been widely used in drug discovery and is a topic of high interest. Here a deep conditional transformer neural network, SyntaLinker, was applied for the scaffold hopping of a phase III clinical Akt inhibitor, AZD5363. A number of novel scaffolds were generated and compound 1a as a proof-of-concept was synthesized and validated by biochemical assay. Further structure-based optimization of 1a led to a novel Akt inhibitor with high potency (Akt1 IC50 = 88 nM) and in vitro antitumor activities.

引用

页码：10588 / 10591

页数：4

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