scholarly journals Quantitative Structure–Activity Relationships of Natural-Product-Inspired, Aminoalkyl-Substituted 1-Benzopyrans as Novel Antiplasmodial Agents

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5249
Author(s):  
Friederike M. Wunsch ◽  
Bernhard Wünsch ◽  
Freddy A. Bernal ◽  
Thomas J. Schmidt
Keyword(s):  
3D Qsar ◽  
Structure Activity Relationship ◽  
Structural Features ◽  
Field Analysis ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Activity Data ◽  
Structure Activity

On the basis of the finding that various aminoalkyl-substituted chromene and chromane derivatives possess strong and highly selective in vitro bioactivity against Plasmodium falciparum, the pathogen responsible for tropical malaria, we performed a structure–activity relationship study for such compounds. With structures and activity data of 52 congeneric compounds from our recent studies, we performed a three-dimensional quantitative structure–activity relationship (3D-QSAR) study using the comparative molecular field analysis (CoMFA) approach as implemented in the Open3DQSAR software. The resulting model displayed excellent internal and good external predictive power as well as good robustness. Besides insights into the molecular interactions and structural features influencing the antiplasmodial activity, this model now provides the possibility to predict the activity of further untested compounds to guide our further synthetic efforts to develop even more potent antiplasmodial chromenes/chromanes.

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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