American Chemical Society
ml2c00515_si_002.xlsx (347.02 kB)

Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design

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posted on 2023-02-08, 20:13 authored by Yang Yu, Junhong Huang, Hu He, Jing Han, Geyan Ye, Tingyang Xu, Xianqiang Sun, Xiumei Chen, Xiaoming Ren, Chunlai Li, Huijuan Li, Wei Huang, Yangyang Liu, Xinjuan Wang, Yongzhi Gao, Nianhe Cheng, Na Guo, Xibo Chen, Jianxia Feng, Yuxia Hua, Chong Liu, Guoyun Zhu, Zhi Xie, Lili Yao, Wenge Zhong, Xinde Chen, Wei Liu, Hailong Li
Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration.