5-aminobenzofuran-2-sulfonamide | 124069-34-7

• 分子结构分类: 有机化合物 - 有机杂环化合物 - 苯并呋喃
• 英文名称: 5-aminobenzofuran-2-sulfonamide
• 英文别名: 5-Amino-2-benzofuransulfonamide；5-amino-1-benzofuran-2-sulfonamide
• CAS: 124069-34-7
• 化学式: C₈H₈N₂O₃S
• 分子量: 212.229
• InChiKey: CQPVSEQFPABIRR-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  0.5
• 重原子数：
  14
• 可旋转键数：
  1
• 环数：
  2.0
• sp3杂化的碳原子比例：
  0.0
• 拓扑面积：
  108
• 氢给体数：
  2
• 氢受体数：
  5

反应信息

• 作为产物：
  描述：

  1-苯并呋喃-2-磺酰胺 在 盐酸 、 乙醇 、 硝酸 、 铁粉 作用下， 生成 6-aminobenzofuran-2-sulfonamide 、 5-aminobenzofuran-2-sulfonamide
  参考文献：
  名称：

  局部活性碳酸酐酶抑制剂。3.苯并呋喃-和吲哚-2-磺酰胺。

  摘要：

  制备苯并呋喃-和吲哚-2-磺酰胺的衍生物作为局部活性的眼用降压药进行评估。在兔子高眼压症模型中，发现这些化合物是出色的碳酸酐酶抑制剂，并能降低眼内压。然而，由于观察到它们引起豚鼠的皮肤致敏作用而阻止了这些化合物用于临床用途的开发。进一步证明了亲电性（通过与还原型谷胱甘肽的体外反应性评估）与皮肤致敏性之间的相关性。

  DOI：

  10.1021/jm00164a045

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.