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1039358-76-3

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1039358-76-3 Usage

Check Digit Verification of cas no

The CAS Registry Mumber 1039358-76-3 includes 10 digits separated into 3 groups by hyphens. The first part of the number,starting from the left, has 7 digits, 1,0,3,9,3,5 and 8 respectively; the second part has 2 digits, 7 and 6 respectively.
Calculate Digit Verification of CAS Registry Number 1039358-76:
(9*1)+(8*0)+(7*3)+(6*9)+(5*3)+(4*5)+(3*8)+(2*7)+(1*6)=163
163 % 10 = 3
So 1039358-76-3 is a valid CAS Registry Number.

1039358-76-3Downstream Products

1039358-76-3Relevant articles and documents

Experimental and QSAR studies on antimicrobial activity of benzimidazole derivatives

Puratchikody, Ayarivan,Nagalakshmi, Govindasamy,Doble, Mukesh

, p. 273 - 281 (2008/09/21)

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 C 6H4NO2 and R′ as SO2C 6H4-CH3(p), with R as C6H 4OCH3 and R′ as SO2C6H 4-CH3(p), and with R as CH2C6H 5 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 SO 2C6H4-CH3(p) or COC 6H5 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 CH2OC6H 5 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; R2 adj=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.

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