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phosphoric acid mono-[4-(2-cyano-6-methylaminopurin-9-yl)-1-phosphonooxymethyl-bicyclo[3.1.0]hex-2-yl] ester

中文名称
——
中文别名
——
英文名称
phosphoric acid mono-[4-(2-cyano-6-methylaminopurin-9-yl)-1-phosphonooxymethyl-bicyclo[3.1.0]hex-2-yl] ester
英文别名
[(1R,2S,4S,5S)-4-[2-cyano-6-(methylamino)purin-9-yl]-2-phosphonooxy-1-bicyclo[3.1.0]hexanyl]methyl dihydrogen phosphate
phosphoric acid mono-[4-(2-cyano-6-methylaminopurin-9-yl)-1-phosphonooxymethyl-bicyclo[3.1.0]hex-2-yl] ester化学式
CAS
——
化学式
C14H18N6O8P2
mdl
——
分子量
460.28
InChiKey
AZMCJQGMBZKTHT-JWUFEOHASA-N
BEILSTEIN
——
EINECS
——
  • 物化性质
  • 计算性质
  • ADMET
  • 安全信息
  • SDS
  • 制备方法与用途
  • 上下游信息
  • 反应信息
  • 文献信息
  • 表征谱图
  • 同类化合物
  • 相关功能分类
  • 相关结构分类

计算性质

  • 辛醇/水分配系数(LogP):
    -2.5
  • 重原子数:
    30
  • 可旋转键数:
    7
  • 环数:
    4.0
  • sp3杂化的碳原子比例:
    0.57
  • 拓扑面积:
    213
  • 氢给体数:
    5
  • 氢受体数:
    13

反应信息

  • 作为产物:
    参考文献:
    名称:
    P2Y1 Antagonists:  Combining Receptor-Based Modeling and QSAR for a Quantitative Prediction of the Biological Activity Based on Consensus Scoring
    摘要:
    P2Y(1) is an ADP-activated G protein-coupled receptor (GPCR). Its antagonists impede platelet aggregation in vivo and are potential antithrombotic agents. Combining ligand and structure-based modeling we generated a consensus model (LIST-CM) correlating antagonist structures with their potencies. We docked 45 antagonists into our rhodopsin-based human P2Y(1) homology model and calculated docking scores and free binding energies with the Linear Interaction Energy (LIE) method in continuum-solvent. The resulting alignment was also used to build QSAR based on CoMFA, CoMSIA, and molecular descriptors. To benefit from the strength of each technique and compensate for their limitations, we generated our LIST-CM with a PLS regression based on the predictions of each methodology. A test set featuring untested substituents was synthesized and assayed in inhibition of 2-MeSADP-stimulated PLC activity and in radioligand binding. LIST-CM outperformed internal and external predictivity of any individual model to predict accurately the potency of 75% of the test set.
    DOI:
    10.1021/jm0700971
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