3-(2-imidazo[1,2-a]pyrazin-5-ylethynyl)-4-methyl-N-[4-[(4-methylpiperazin-1-yl)methyl]-3-(trifluoromethyl)phenyl]benzamide | 1428445-63-9

• 分子结构分类: 有机化合物 - 苯类化合物 - 苯和取代衍生物
• 英文名称: 3-(2-imidazo[1,2-a]pyrazin-5-ylethynyl)-4-methyl-N-[4-[(4-methylpiperazin-1-yl)methyl]-3-(trifluoromethyl)phenyl]benzamide
• CAS: 1428445-63-9
• 化学式: C₂₉H₂₇F₃N₆O
• 分子量: 532.568
• InChiKey: VSJSIOZQNZPUAV-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  4.5
• 重原子数：
  39
• 可旋转键数：
  6
• 环数：
  5.0
• sp3杂化的碳原子比例：
  0.28
• 拓扑面积：
  65.8
• 氢给体数：
  1
• 氢受体数：
  8

反应信息

• 作为产物：
  描述：

  5-ethynylimidazo[1,2-a]pyrazine 、 alkaline earth salt of/the/ methylsulfuric acid 在 copper(l) iodide 、 四(三苯基膦)钯 、 N,N-二异丙基乙胺 作用下， 以 N,N-二甲基甲酰胺 为溶剂， 反应 0.78h， 以31%的产率得到3-(2-imidazo[1,2-a]pyrazin-5-ylethynyl)-4-methyl-N-[4-[(4-methylpiperazin-1-yl)methyl]-3-(trifluoromethyl)phenyl]benzamide
  参考文献：
  名称：

  Rapid Discovery of a Novel Series of Abl Kinase Inhibitors by Application of an Integrated Microfluidic Synthesis and Screening Platform

  摘要：

  Drug discovery faces economic and scientific imperatives to deliver lead molecules rapidly and efficiently. Using traditional paradigms the molecular design, synthesis, and screening loops enforce a significant time delay leading to inefficient use of data in the iterative molecular design process. Here, we report the application of a flow technology platform integrating the key elements of structure activity relationship (SAR) generation to the discovery of novel Abl kinase inhibitors. The platform utilizes flow chemistry for rapid in-line synthesis, automated purification, and analysis coupled with bioassay. The combination of activity prediction using Random-Forest regression with chemical space sampling algorithms allows the construction of an activity model that refines itself after every iteration of synthesis and biological result. Within just 21 compounds, the automated process identified a novel template and hinge binding motif with pIC(50) > 8 against Abl kinase - both wild type and clinically relevant mutants. Integrated microfluidic synthesis and screening coupled with machine learning design have the potential to greatly reduce the time and cost of drug discovery within the hit-to-lead and lead optimization phases.

  DOI：

  10.1021/jm400099d