benzyl N-[1-(diethylamino)-1-oxopropan-2-yl]carbamate | 42166-75-6

• 分子结构分类: 有机化合物 - 有机酸及其衍生物 - 羧酸及其衍生物
• 英文名称: benzyl N-[1-(diethylamino)-1-oxopropan-2-yl]carbamate
• CAS: 42166-75-6
• 化学式: C₁₅H₂₂N₂O₃
• 分子量: 278.351
• InChiKey: PVORWVHRGGXSBO-UHFFFAOYSA-N

物化性质

• 沸点：
  434.2±38.0 °C(Predicted)
• 密度：
  1.092±0.06 g/cm3(Predicted)

计算性质

• 辛醇/水分配系数(LogP)：
  2.1
• 重原子数：
  20
• 可旋转键数：
  7
• 环数：
  1.0
• sp3杂化的碳原子比例：
  0.466
• 拓扑面积：
  58.6
• 氢给体数：
  1
• 氢受体数：
  3

反应信息

• 作为产物：
  描述：

  N-CBZ-DL-丙氨酸 、 二乙胺 在 N-甲基吗啉 、 氯甲酸异丁酯 作用下， 以 四氢呋喃 为溶剂， 反应 1.31h， 以67%的产率得到benzyl N-[1-(diethylamino)-1-oxopropan-2-yl]carbamate
  参考文献：
  名称：

  Application of Predictive QSAR Models to Database Mining:  Identification and Experimental Validation of Novel Anticonvulsant Compounds

  摘要：

  We have developed a drug discovery strategy that employs variable selection quantitative structure-activity relationship (QSAR) models for chemical database mining. The approach starts with the development of rigorously validated QSAR models obtained with the variable selection k nearest neighbor (kNN) method (or, in principle, with any other robust model-building technique). Model validation. is based on several statistical criteria, including the randomization of the target property (Y-randomization), independent assessment of the training set model's predictive power using external test sets, and the establishment of the model's applicability domain. All successful models are employed in database mining concurrently; in each case, only variables selected as a result of model building (termed descriptor pharmacophore) are used in chemical similarity searches comparing active compounds of the training set (queries) with those in chemical databases. Specific biological activity (characteristic of the training set compounds) of external database entries found to be within a predefined similarity threshold of the training set molecules is predicted on the basis of the validated QSAR models using the applicability domain criteria. Compounds judged to have high predicted activities by all or the majority of all models are considered as consensus hits. We report on the application of this computational strategy for the first time for the discovery of anticonvulsant agents in the Maybridge and National Cancer Institute (NCI) databases containing ca. 250 000 compounds combined. Forty-eight anticonvulsant agents of the functionalized amino acid (FAA) series were used to build kNN variable selection QSAR models. The 10 best models were applied to mining chemical databases, and 22 compounds were selected as consensus hits. Nine compounds were synthesized and tested at the NIH Epilepsy Branch, Rockville, MD using the same biological test that was employed to assess the anticonvulsant activity of the training set compounds; of these nine, four were exact database hits and five were derived from the hits by minor chemical modifications. Seven of these nine compounds were confirmed to be active, indicating an exceptionally high hit rate. The approach described in this report can be used as a general rational drug discovery tool.

  DOI：

  10.1021/jm030584q

文献信息

• Application of Predictive QSAR Models to Database Mining:  Identification and Experimental Validation of Novel Anticonvulsant Compounds
  作者：Min Shen、Cécile Béguin、Alexander Golbraikh、James P. Stables、Harold Kohn、Alexander Tropsha

  DOI：10.1021/jm030584q

  日期：2004.4.1

  We have developed a drug discovery strategy that employs variable selection quantitative structure-activity relationship (QSAR) models for chemical database mining. The approach starts with the development of rigorously validated QSAR models obtained with the variable selection k nearest neighbor (kNN) method (or, in principle, with any other robust model-building technique). Model validation. is based on several statistical criteria, including the randomization of the target property (Y-randomization), independent assessment of the training set model's predictive power using external test sets, and the establishment of the model's applicability domain. All successful models are employed in database mining concurrently; in each case, only variables selected as a result of model building (termed descriptor pharmacophore) are used in chemical similarity searches comparing active compounds of the training set (queries) with those in chemical databases. Specific biological activity (characteristic of the training set compounds) of external database entries found to be within a predefined similarity threshold of the training set molecules is predicted on the basis of the validated QSAR models using the applicability domain criteria. Compounds judged to have high predicted activities by all or the majority of all models are considered as consensus hits. We report on the application of this computational strategy for the first time for the discovery of anticonvulsant agents in the Maybridge and National Cancer Institute (NCI) databases containing ca. 250 000 compounds combined. Forty-eight anticonvulsant agents of the functionalized amino acid (FAA) series were used to build kNN variable selection QSAR models. The 10 best models were applied to mining chemical databases, and 22 compounds were selected as consensus hits. Nine compounds were synthesized and tested at the NIH Epilepsy Branch, Rockville, MD using the same biological test that was employed to assess the anticonvulsant activity of the training set compounds; of these nine, four were exact database hits and five were derived from the hits by minor chemical modifications. Seven of these nine compounds were confirmed to be active, indicating an exceptionally high hit rate. The approach described in this report can be used as a general rational drug discovery tool.