2-N-[4-(2-chlorophenoxy)phenyl]pyrimidine-2,4-diamine | 1366234-32-3

• 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: 2-N-[4-(2-chlorophenoxy)phenyl]pyrimidine-2,4-diamine
• CAS: 1366234-32-3
• 化学式: C₁₆H₁₃ClN₄O
• 分子量: 312.758
• InChiKey: MRPYQYRDAONMAU-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  3.9
• 重原子数：
  22
• 可旋转键数：
  4
• 环数：
  3.0
• sp3杂化的碳原子比例：
  0.0
• 拓扑面积：
  73.1
• 氢给体数：
  2
• 氢受体数：
  5

反应信息

• 作为产物：
  描述：

  4-氨基-2-氯嘧啶 、 4-(2-氯苯氧基)苯胺 在 盐酸 作用下， 以 甲醇 为溶剂， 以7%的产率得到2-N-[4-(2-chlorophenoxy)phenyl]pyrimidine-2,4-diamine
  参考文献：
  名称：

  The discovery of aminopyrazines as novel, potent Nav1.7 antagonists: Hit-to-lead identification and SAR

  摘要：

  Herein the discovery of a novel class of aminoheterocyclic Na(v)1.7 antagonists is reported. Hit compound 1 was potent but suffered from poor pharmacokinetics and selectivity. The compact structure of 1 offered a modular synthetic strategy towards a broad structure-activity relationship analysis. This analysis led to the identification of aminopyrazine 41, which had vastly improved hERG selectivity and pharmacokinetic properties. (C) 2012 Elsevier Ltd. All rights reserved.

  DOI：

  10.1016/j.bmcl.2012.01.023

文献信息

• SYSTEMS AND METHODS FOR PREDICTING CARDIOTOXICITY OF MOLECULAR PARAMETERS OF A COMPOUND BASED ON MACHINE LEARNING ALGORITHMS
  申请人：UTI Limited Partnership

  公开号：US20180172667A1

  公开（公告）日：2018-06-21

  Systems and methods are provided for predicting cardiotoxicity of molecular parameters of a compound. A computer can provide as input to a machine learning algorithm the molecular parameters of the compound. The molecular parameters can include at least structural information about the compound. The machine learning algorithm can have been trained using respective molecular parameters of compounds known to have cardiotoxicity and of compounds known not to have cardiotoxicity. The computer can receive as output from the machine learning algorithm a representation of the predicted cardiotoxicity of each molecular parameter of at least a subset of the molecular parameters of the compound.
• [EN] SYSTEMS AND METHODS FOR PREDICTING CARDIOTOXICITY OF MOLECULAR PARAMETERS OF A COMPOUND BASED ON MACHINE LEARNING ALGORITHMS<br/>[FR] SYSTÈMES ET PROCÉDÉS PERMETTANT DE PRÉDIRE LA CARDIOTOXICITÉ DE PARAMÈTRES MOLÉCULAIRES D'UN COMPOSÉ SUR LA BASE D'ALGORITHMES D'APPRENTISSAGE MACHINE
  申请人：UTI LIMITED PARTNERSHIP

  公开号：WO2016201575A1

  公开（公告）日：2016-12-22

  Systems and methods are provided for predicting cardiotoxicity of molecular parameters of a compound. A computer can provide as input to a machine learning algorithm the molecular parameters of the compound. The molecular parameters can include at least structural information about the compound. The machine learning algorithm can have been trained using respective molecular parameters of compounds known to have cardiotoxicity and of compounds known not to have cardiotoxicity. The computer can receive as output from the machine learning algorithm a representation of the predicted cardiotoxicity of each molecular parameter of at least a subset of the molecular parameters of the compound.
• The discovery of aminopyrazines as novel, potent Nav1.7 antagonists: Hit-to-lead identification and SAR
  作者：Howard Bregman、Hanh Nho Nguyen、Elma Feric、Joseph Ligutti、Dong Liu、Jeff S. McDermott、Ben Wilenkin、Anruo Zou、Liyue Huang、Xingwen Li、Stefan I. McDonough、Erin F. DiMauro

  DOI：10.1016/j.bmcl.2012.01.023

  日期：2012.3

  Herein the discovery of a novel class of aminoheterocyclic Na(v)1.7 antagonists is reported. Hit compound 1 was potent but suffered from poor pharmacokinetics and selectivity. The compact structure of 1 offered a modular synthetic strategy towards a broad structure-activity relationship analysis. This analysis led to the identification of aminopyrazine 41, which had vastly improved hERG selectivity and pharmacokinetic properties. (C) 2012 Elsevier Ltd. All rights reserved.