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1-(4-Methylphenyl)sulfonyl-2-(phenoxymethyl)benzimidazole | 439574-26-2

中文名称
——
中文别名
——
英文名称
1-(4-Methylphenyl)sulfonyl-2-(phenoxymethyl)benzimidazole
英文别名
——
1-(4-Methylphenyl)sulfonyl-2-(phenoxymethyl)benzimidazole化学式
CAS
439574-26-2
化学式
C21H18N2O3S
mdl
——
分子量
378.452
InChiKey
KPNLOFMSDYAFDT-UHFFFAOYSA-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):
    4.5
  • 重原子数:
    27
  • 可旋转键数:
    5
  • 环数:
    4.0
  • sp3杂化的碳原子比例:
    0.1
  • 拓扑面积:
    69.6
  • 氢给体数:
    0
  • 氢受体数:
    4

反应信息

  • 作为产物:
    描述:
    2-(苯氧基甲基)-1H-苯并咪唑对甲苯磺酰氯sodium hydroxide 作用下, 以95%的产率得到1-(4-Methylphenyl)sulfonyl-2-(phenoxymethyl)benzimidazole
    参考文献:
    名称:
    Experimental and QSAR Studies on Antimicrobial Activity of Benzimidazole Derivatives
    摘要:
    已经合成了28种苯并咪唑类衍生物,并对它们对四种细菌(金黄色葡萄球菌、大肠杆菌、短小芽孢杆菌和普通变形杆菌)和两种真菌(黄曲霉和黑曲霉)的抗微生物活性进行了测试。当R为C6H4NO2、R′为SO2C6H4-CH3(p),R为C6H4OCH3、R′为SO2C6H4-CH3(p),以及R为CH2C6H5、R′为CH2(CH2)9Cl时,这些化合物的抗菌活性与环丙沙星相当或更高,对金黄色葡萄球菌和大肠杆菌的活性高于制霉菌素对黄曲霉的活性。还有几种化合物对大肠杆菌的活性优于标准抗生素。R为CCl3、R′为SO2C6H4-CH3(p)或COC6H5的化合物对所有生物体的活性最低。在R′位置添加亚甲基基团可以提高活性。许多化合物对一种或多种生物体的活性优于环丙沙星。R为CH2OC6H5、R′为CH2(CH2)9Cl的化合物对这两种真菌的活性高于对照制霉菌素。开发的定量结构-活性关系(QSARs)对所有生物体都很好(R2=0.65至0.88;Radj2 = 0.63至0.86),开发模型的预测能力也合理(q2=0.52至0.83)。这些模型包含两到三个独立变量。具有三个独立变量的模型的数据被分为训练集和测试/验证集。前者用于开发QSAR,这些开发的模型用于预测测试集数据的活性。在所有三种情况下,模型的预测能力都很好。识别的分子描述符主要是log P、电子参数、分子大小、形状和面积。抗菌活性与第一主成分之间存在正相关关系。
    DOI:
    10.1248/cpb.56.273
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文献信息

  • Experimental and QSAR Studies on Antimicrobial Activity of Benzimidazole Derivatives
    作者:Ayarivan Puratchikody、Govindasamy Nagalakshmi、Mukesh Doble
    DOI:10.1248/cpb.56.273
    日期:——
    Twenty eight analogues of benzimidazoles had been synthesized and tested for their antimicrobial activity against four bacteria (Staphylococcus auerus, Escherichia coli, Bacillus pumilus and Proteus vulgaris) and two fungi (Aspergillus flavus and Aspergilus niger). Compounds with R as C6H4NO2 and R′ as SO2C6H4–CH3(p), with R as C6H4OCH3 and R′ as SO2C6H4–CH3(p), and with R as CH2C6H5 and R′ as CH2(CH2)9Cl exhibited comparable or higher antibacterial activity than Ciprofloxacin against S. auerus and E. coli and, higher activity than Nystatin against A. flavus. Several other compounds showed better activity than the standard antibiotic for E. coli. Compounds with R as CCl3 and R′ as SO2C6H4–CH3(p) or COC6H5 exhibited the lowest activity against all the organisms. Addition of methylene groups in the R′ position increased activity. Many of the compounds showed better activity than Ciprofloxacin for one or more organisms. Compound with R as CH2OC6H5 and R′ as CH2(CH2)9Cl exhibited higher activity against both the fungii than the control Nystatin. Quantitative structure activity relationships (QSARs) developed were good for all the organisms (R2=0.65 to 0.88; Radj2 = 0.63 to 0.86) and the predictive capability of the developed models was also reasonable (q2=0.52 to 0.83). The models had two to three independent variables. The data for the models which had three independent variables were divided into training and test/validation sets. The former set was used to develop the QSAR and these developed models were used to predict the activity of the test set data. In all the three cases the predictive capability of the models was good. The molecular descriptors identified were predominantly log P, electronic parameters, molecular size, shape and area. A positive correlation existed between the antibacterial activity and the first principal component.
    已经合成了28种苯并咪唑类衍生物,并对它们对四种细菌(金黄色葡萄球菌、大肠杆菌、短小芽孢杆菌和普通变形杆菌)和两种真菌(黄曲霉和黑曲霉)的抗微生物活性进行了测试。当R为C6H4NO2、R′为SO2C6H4-CH3(p),R为C6H4OCH3、R′为SO2C6H4-CH3(p),以及R为CH2C6H5、R′为CH2(CH2)9Cl时,这些化合物的抗菌活性与环丙沙星相当或更高,对金黄色葡萄球菌和大肠杆菌的活性高于制霉菌素对黄曲霉的活性。还有几种化合物对大肠杆菌的活性优于标准抗生素。R为CCl3、R′为SO2C6H4-CH3(p)或COC6H5的化合物对所有生物体的活性最低。在R′位置添加亚甲基基团可以提高活性。许多化合物对一种或多种生物体的活性优于环丙沙星。R为CH2OC6H5、R′为CH2(CH2)9Cl的化合物对这两种真菌的活性高于对照制霉菌素。开发的定量结构-活性关系(QSARs)对所有生物体都很好(R2=0.65至0.88;Radj2 = 0.63至0.86),开发模型的预测能力也合理(q2=0.52至0.83)。这些模型包含两到三个独立变量。具有三个独立变量的模型的数据被分为训练集和测试/验证集。前者用于开发QSAR,这些开发的模型用于预测测试集数据的活性。在所有三种情况下,模型的预测能力都很好。识别的分子描述符主要是log P、电子参数、分子大小、形状和面积。抗菌活性与第一主成分之间存在正相关关系。
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