Diffusion of arylpropionate non-steroidal anti-inflammatory drugs into the cerebrospinal fluid: a quantitative structure-activity relationship approach

2004 ◽  
Vol 18 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Fabienne Pehourcq ◽  
Myriam Matoga ◽  
Bernard Bannwarth
Keyword(s):  
Cerebrospinal Fluid ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Inflammatory Drugs ◽  
Anti Inflammatory

scholarly journals QSAR, chalcones derivatives, tum Quantitative structure-activity relationship studies of series of chalcones derivatives as inhibitors of tumor necrosis factor-alpha

2020 ◽  
Vol 25 (3) ◽  
pp. 1-14
Author(s):  
Wisam Alhassan ◽  
Sadiq M. H. Ismae ◽  
Jasim M. Al-Shawi
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Necrosis Factor Alpha ◽  
Structure Activity ◽  
Physicochemical Descriptors ◽  
Biological Effectiveness ◽  
Factor Alpha ◽  
Anti Inflammatory

Quantitative structure-activity relationship (QSAR) teqnique was used to predict the biological activity of a series of chalcones compounds as anti-inflammatory. 26 physicochemical descriptors are tested in QSAR equations configuration to predict biological effectiveness of compounds under study.  The values of R2 in Eqs (1–3) ranged from 0.794–0.873, the F values ranged from 14.161–26.206 and the S values ranged from 0.262–0.334. The results demonstrated excellent models based on Eq.3, along with high of R2, F and minimum S by employing three parameters r(C3-C5), (LUMO+1) and (LUMO+2). This signifies that these parameters play a significant role in determining anti-inflammatory characteristics.  

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure- Activity Relationship

2019 ◽  
Vol 16 ◽  
Author(s):  
Meysam Shirmohammadi ◽  
Zakiyeh Bayat ◽  
Esmat Mohammadinasab
Keyword(s):  
Partition Coefficient ◽  
Principal Component ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Selection Methods ◽  
Qsar Study ◽  
Structure Activity ◽  
Descriptor Selection

: Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using random selection method. Second, three approaches including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, with the aim of examining the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. QSAR study showed that the both regression and descriptor selection methods have vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. The proposed expression by MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

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