药物发现
激酶
标杆管理
从长凳到床边
药品
计算生物学
计算机科学
药理学
化学
医学
业务
生物化学
生物
医学物理学
营销
作者
Tian‐Hua Wei,Shuangshuang Zhou,Xiaolong Jing,Jia‐Chuan Liu,Meng Sun,Z Zhao,Qingqing Li,Zixuan Wang,Jin Yang,Yun Zhou,Xue Wang,Cheng-Xiao Ling,Ning Ding,Xin Xue,Yan‐Cheng Yu,Xiaolong Wang,Xiaoying Yin,Shan‐Liang Sun,Peng Cao,Nian‐Guang Li
标识
DOI:10.1021/acs.jcim.4c01830
摘要
Developing selective kinase inhibitors remains a formidable challenge in drug discovery because of the highly conserved structural information on adenosine triphosphate (ATP) binding sites across the kinase family. Tailoring docking protocols to identify promising kinase inhibitor candidates for optimization has long been a substantial obstacle to drug discovery. Therefore, we introduced "Kinase-Bench," a pioneering benchmark suite designed for an advanced virtual screen, to improve the selectivity and efficacy of kinase inhibitors. Our comprehensive data set includes 6875 selective ligands and 422,799 decoys for 75 kinases, using extensive bioactivity and structural data from the ChEMBL database and decoys generated by the Directory of Useful Decoys-Enhanced version. Our benchmarking sets and retrospective case studies were designed to provide useful guidance in discovering selective kinase inhibitors. We employed a Glide High-Throughput Virtual Screen and Standard Precision complemented by three scoring functions and customized protein-ligand interaction filters that target specific kinase residue interactions. These innovations were successfully implemented in our virtual screen efforts targeting JAK1 inhibitors, achieving selectivity against its family member, TYK2. Consequently, we identified novel potential hits: Compound
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