文献类型：期刊文献

英文题名：RingKin: portraying the vast macrocyclic chemical universe surrounding kinase drugs

作者：Diao, Yanyan[1];Hu, Feng[2];Jiang, Wenzhe[2];Hu, Yuting[2];Ma, Shanjie[2];Wu, Dawei[2];Tong, Lingrui[2];Xiang, Sutong[1];Chen, Chengjie[1];Li, Zetong[1];Shen, Zihao[2];Zhao, Zhenjiang[2];Xu, Yufang[2];Li, Jianhua[2];Li, Honglin[1]

机构：[1]East China Normal Univ, Innovat Ctr AI & Drug Discovery, Sch Pharm, 3663 North Zhongshan Rd, Shanghai 200062, Peoples R China;[2]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2026

外文期刊名：CHEMICAL SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001857120800001)】；

基金：The research is supported in part by the National Natural Science Foundation of China (grants 82425104 and 82404517), the Science and Technology Commission of Shanghai Municipality (No. 24JS2830200), the National Key Research and Development Program of China (2022YFC3400501 and 2022YFC3400504), and the Fundamental Research Funds for the Central Universities.

语种：英文

摘要：Cyclization strategies have emerged as a compelling approach to overcome persistent challenges in kinase drug development, notably poor selectivity and unfavorable pharmacological properties. Existing studies remain limited to individual kinases or retrospective analyses, while systematic exploration of the potentially vast and underexplored macrocyclic space for kinase modulation is still lacking. By leveraging artificial intelligence-based methods, we pioneered the creation of RingKin, an immense chemical universe encompassing 72.27 million macrocycles generated from 495 approved or clinical-stage kinase drugs. In addition to diverse macrocyclic scaffolds, RingKin offers approximately 1.8 billion model-estimated property annotations by 15 benchmarked deep learning or machine learning-based models, supporting the multidimensional characterization of generated macrocycles, including kinase selectivity profiles, physicochemical and ADMET features, and target associations. Systematic analyses suggest that these macrocycles have the potential to mitigate several major limitations associated with current kinase drugs. Our work portrays the prospective macrocyclic chemical landscape surrounding existing kinase drugs, establishing RingKin as a hypothesis-generating resource for macrocycle exploration and drug discovery. From macrocyclic derivatives of the approved pan-FGFR inhibitor erdafitinib, three preliminary FGFR2-preferring hit compounds were identified, demonstrating the utility of RingKin as an open-access resource for kinase-targeted drug design.