N-(((4-methoxyphenyl)thio)(phenyl)methyl)benzamide | 1327162-86-6

• 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: N-(((4-methoxyphenyl)thio)(phenyl)methyl)benzamide
• 英文别名: (R)-N-(((4-methoxyphenyl)thio)(phenyl)methyl)benzamide；N-[[(4-methoxyphenyl)sulfanyl](phenyl)methyl]benzamide；N-[(R)-(4-methoxyphenyl)sulfanyl-phenylmethyl]benzamide
• CAS: 1327162-86-6
• 化学式: C₂₁H₁₉NO₂S
• 分子量: 349.453
• InChiKey: STFIEXDREVANEU-OAQYLSRUSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  5
• 重原子数：
  25
• 可旋转键数：
  6
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.1
• 拓扑面积：
  63.6
• 氢给体数：
  1
• 氢受体数：
  3

反应信息

• 作为产物：
  描述：

  4-甲氧基苯硫酚 、 N-benzylidenebenzamide 在 (11bS)-4-羟基-2,6-二[2,4,6-三(异丙基)苯基]-二萘并[2,1-d:1',2'-f][1,3,2]二氧杂磷杂卓4-氧化物 作用下， 以 甲苯 为溶剂， 反应 0.17h， 以96%的产率得到N-(((4-methoxyphenyl)thio)(phenyl)methyl)benzamide
  参考文献：
  名称：

  手性磷酸催化硫醇与 N-酰基亚胺的加成：获得手性 N,S-乙缩醛

  摘要：

  报道了使用手性 BINOL 磷酸制备富含对映体的N , S-缩醛的第一种催化不对称方法。该反应将N-酰基亚胺与硫醇结合，以优异的产率和对映选择性生成产物。加成反应也可以通过特殊的底物与催化剂 (S/C) 摩尔比来实现。富电子和缺电子的芳香族N-酰基亚胺，以及广泛的脂肪族和芳香族硫醇，都表现出优异的反应性。

  DOI：

  10.1021/ol201899c

文献信息

• A robust heterogeneous chiral phosphoric acid enables multi decagram scale production of optically active <i>N</i>,<i>S</i>-ketals
  作者：Aitor Maestro、Bhanwar K. Malviya、Gerald Auer、Sándor B. Ötvös、C. Oliver Kappe

  DOI：10.1039/d4gc00019f

  日期：——

  need to be addressed. Developing new and reliable enantioselective processes for reproducing batch reactions on a large scale requires a combination of chemical and technical solutions. In this manuscript, we combine a robust immobilized chiral phosphoric acid with a new packed-bed reactor design. This combination allows scaling up of the enantioselective addition of thiols to imines from a few milligrams

  不对称有机催化被IUPAC评为2019年化学界“十大新兴技术”之一。它使化学过程变得更加可持续的潜力是有前景的，但仍然存在需要解决的挑战。开发新的、可靠的对映选择性工艺来大规模重现间歇反应需要化学和技术解决方案的结合。在这份手稿中，我们将坚固的固定手性磷酸与新的填充床反应器设计相结合。这种组合允许在连续流动过程中将硫醇与亚胺的对映选择性加成规模从几毫克扩大到数十克规模，而不会导致催化剂的物理或化学降解。
• Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis
  作者：Jeremy J. Henle、Andrew F. Zahrt、Brennan T. Rose、William T. Darrow、Yang Wang、Scott E. Denmark

  DOI：10.1021/jacs.0c04715

  日期：2020.7.1

  Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.