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.