JR-202 | 1428637-30-2

• 分子结构分类: 有机化合物 - 脂质和类脂质分子 - 异戊烯醇脂质
• 英文名称: JR-202
• 英文别名: 2-[(4-propan-2-ylphenyl)methylideneamino]guanidine;hydrochloride
• CAS: 1428637-30-2
• 化学式: C₁₁H₁₆N₄*ClH
• 分子量: 240.736
• InChiKey: KRWUGYXMXJBFQU-UHFFFAOYSA-N

计算性质

• 辛醇/水分配系数(LogP)：
  2.05
• 重原子数：
  16.0
• 可旋转键数：
  3.0
• 环数：
  1.0
• sp3杂化的碳原子比例：
  0.27
• 拓扑面积：
  74.26
• 氢给体数：
  3.0
• 氢受体数：
  2.0

反应信息

• 作为产物：
  描述：

  肼甲酰亚胺酰胺一氯化氢 、 4-异丙基苯甲醛 以 甲醇 为溶剂， 生成 JR-202
  参考文献：
  名称：

  Improving the inhibitory activity of arylidenaminoguanidine compounds at the N-methyl-d-aspartate receptor complex from a recursive computational-experimental structure–activity relationship study

  摘要：

  Using a combination of both the partial least squares (PLS) and back-propagation artificial neural network (ANN) pattern recognition methods, several models have been developed to predict the activity of a series of arylidenaminoguanidine analogs as inhibitory modulators of the N-methyl-D-aspartate receptor complex. This was done by correlating structural and physicochemical descriptors obtained from computation software with the experimentally observed [H-3]MK-801 displacement ability of a small library of synthesized and in vitro screened arylidenaminoguanidines. Results for the generated PLS model were r(2) = 0.814, rmsd = 0.208, r(CV)(2) = 0.714, loormsd = 0.261. The ANN model was created utilizing the eleven descriptors from the PLS model for comparison. The quality of the ANN model (r(2)=0.828, rmsd = 0.200, r(CV)(2) = 0.721, loormsd = 0.257) is similar to the PLS model, and indicates that the feature between the inputs and the output is majorly linear. These computational models were able to predict inhibition of the NMDA receptor complex by this series of compounds in silico, affording a predictive structure-based 'pre-screening' paradigm for the arylideneaminoguanidine analogs. (C) 2013 Elsevier Ltd. All rights reserved.

  DOI：

  10.1016/j.bmc.2013.01.051

文献信息

• Improving the inhibitory activity of arylidenaminoguanidine compounds at the N-methyl-d-aspartate receptor complex from a recursive computational-experimental structure–activity relationship study
  作者：Joshua R. Ring、Fang Zheng、Aaron J. Haubner、John M. Littleton、Peter A. Crooks

  DOI：10.1016/j.bmc.2013.01.051

  日期：2013.4

  Using a combination of both the partial least squares (PLS) and back-propagation artificial neural network (ANN) pattern recognition methods, several models have been developed to predict the activity of a series of arylidenaminoguanidine analogs as inhibitory modulators of the N-methyl-D-aspartate receptor complex. This was done by correlating structural and physicochemical descriptors obtained from computation software with the experimentally observed [H-3]MK-801 displacement ability of a small library of synthesized and in vitro screened arylidenaminoguanidines. Results for the generated PLS model were r(2) = 0.814, rmsd = 0.208, r(CV)(2) = 0.714, loormsd = 0.261. The ANN model was created utilizing the eleven descriptors from the PLS model for comparison. The quality of the ANN model (r(2)=0.828, rmsd = 0.200, r(CV)(2) = 0.721, loormsd = 0.257) is similar to the PLS model, and indicates that the feature between the inputs and the output is majorly linear. These computational models were able to predict inhibition of the NMDA receptor complex by this series of compounds in silico, affording a predictive structure-based 'pre-screening' paradigm for the arylideneaminoguanidine analogs. (C) 2013 Elsevier Ltd. All rights reserved.