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

  • ACS Med Chem Lett. 2023 Feb 8;14(3):297-304. doi: 10.1021/acsmedchemlett.2c00515.
Yang Yu  1 ,  Junhong Huang  1 ,  Hu He  2 ,  Jing Han  2 ,  Geyan Ye  1 ,  Tingyang Xu  1 ,  Xianqiang Sun  2 ,  Xiumei Chen  2 ,  Xiaoming Ren  2 ,  Chunlai Li  2 ,  Huijuan Li  2 ,  Wei Huang  2 ,  Yangyang Liu  2 ,  Xinjuan Wang  2 ,  Yongzhi Gao  2 ,  Nianhe Cheng  2 ,  Na Guo  3 ,  Xibo Chen  3 ,  Jianxia Feng  4 ,  Yuxia Hua  4 ,  Chong Liu  4 ,  Guoyun Zhu  2 ,  Zhi Xie  2 ,  Lili Yao  2 ,  Wenge Zhong  2 ,  Xinde Chen  1 ,  Wei Liu  1 ,  Hailong Li  2
Affiliations
  • 1. Tencent AI Lab, Tencent, Shenzhen 518057, China.
  • 2. Regor Therapeutics Group, Shanghai, 201210, China.
  • 3. BioDuro-Sundia, Shanghai, 200131, China.
  • 4. BioDuro-Sundia, Beijing, 102200, China.
PMID: 36923916 DOI: 10.1021/acsmedchemlett.2c00515
Abstract

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.

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