Quantitative Structure-Activity Relationship (QSAR) Study with a Series of 17α-Derivatives of Estradiol: Model for the Development of Reversible Steroid Sulfatase Inhibitors

2009 ◽  
Vol 28 (11-12) ◽  
pp. 1284-1299 ◽  
Author(s):  
René Maltais ◽  
Diane Fournier ◽  
Donald Poirier
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Steroid Sulfatase ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Structure Activity ◽  
Derivatives Of

Synthesis and quantitative structure–activity relationship (QSAR) study of C7-oxime ester derivatives of obacunone as insecticidal agents

RSC Advances ◽  
2015 ◽  
Vol 5 (40) ◽  
pp. 31700-31707 ◽  
Author(s):  
Xiang Yu ◽  
Danfeng Shi ◽  
Xiaoyan Zhi ◽  
Qin Li ◽  
Xiaojun Yao ◽  
...  
Keyword(s):  
Insecticidal Activity ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Positive Control ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Structure Activity ◽  
Derivatives Of

Some compounds exhibited more promising insecticidal activity than toosendanin (a positive control). QSAR model suggested that five descriptors (RDF100v, RDF105u, Dm, Mor15m and R1u) were likely to affect the insecticidal activity of these compounds.

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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