diethyl 2-O-(2,3,4,6-tetra-O-benzyl-α-D-galactopyranosyl) tartrate | 1582794-03-3

• 分子结构分类: 有机化合物 - 有机氧化合物
• 英文名称: diethyl 2-O-(2,3,4,6-tetra-O-benzyl-α-D-galactopyranosyl) tartrate
• CAS: 1582794-03-3
• 化学式: C₄₂H₄₈O₁₁
• 分子量: 728.837
• InChiKey: BCNGZQAOEJEFBY-CLBNAZKYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  5.56
• 重原子数：
  53.0
• 可旋转键数：
  20.0
• 环数：
  5.0
• sp3杂化的碳原子比例：
  0.38
• 拓扑面积：
  128.21
• 氢给体数：
  1.0
• 氢受体数：
  11.0

反应信息

• 作为产物：
  描述：

  Diethyl tartrate 、 2,3,4,6-四-O-苄基-D-吡喃半乳糖 在 indium(III) triflate 作用下， 以 neat (no solvent) 为溶剂， 反应 0.75h， 以36%的产率得到diethyl 2-O-(2,3,4,6-tetra-O-benzyl-α-D-galactopyranosyl) tartrate
  参考文献：
  名称：

  In(III) Triflate-Mediated Solvent-Free Synthesis and Activation of Thioglycosides by Ball Milling and Structural Analysis of Long Chain Alkyl Thioglycosides by TEM and Quantum Chemical Methods

  摘要：

  Conventional solution-phase synthesis of thioglycosides from glycosyl acetates and thiols in the presence of In(III) triflate as reported for benzyl thioglucoside failed when applied to the synthesis of phenolic and alkyl thioglycosides. But, it was achieved in high efficiency and diastereospecificity with ease by solvent-free grinding in a ball mill. The acetates in turn were also obtained by the homogenization of free sugars with stoichiometric amounts of acetic anhydride and catalytic In(OTf)(3) in the mill as neat products. Per-O-benzylated thioglycosides on grinding with an acceptor sugar in the presence of In(OTf)(3) yield the corresponding O-glycosides efficiently. The latter in the case of a difficult secondary alcohol was nearly exclusive (>98%) in 1,2-cis-selectivity. In contrast, the conventional methods for this purpose require use of a coreagent such as NIS along with the Lewis acid to help generate the electrophilic species that actually is responsible for the activation of the thioglycoside donor in situ. The distinctly different self-assembling features of the peracetylated octadecyl 1-thio-alpha-and beta-D-galactopyranosides observed by TEM could be rationalized by molecular modeling.

  DOI：

  10.1021/jo5001753