Toxicity Prediction of Antibiotics on Luminescent Bacteria, Photobacterium phosphoreum, Based on Their Quantitative Structure–Activity Relationship Models

2010 ◽  
Vol 85 (6) ◽  
pp. 550-555 ◽  
Author(s):  
Lei Jiang ◽  
Zhifen Lin ◽  
Xialin Hu ◽  
Daqiang Yin
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Photobacterium Phosphoreum ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Toxicity Prediction ◽  
Structure Activity ◽  
Luminescent Bacteria

scholarly journals A Novel Approach for a Toxicity Prediction Model of Environmental Pollutants by Using a Quantitative Structure-Activity Relationship Method Based on Toxicogenomics

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Junichi Hosoya ◽  
Kumiko Tamura ◽  
Naomi Muraki ◽  
Hiroki Okumura ◽  
Tsuyoshi Ito ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prediction Model ◽  
Environmental Pollutants ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Toxicity Prediction ◽  
Human Lung Cells ◽  
Structure Activity

The development of automobile emission reduction technologies has decreased dramatically the particle concentration in emissions; however, there is a possibility that unexpected harmful chemicals are formed in emissions due to new technologies and fuels. Therefore, we attempted to develop new and efficient toxicity prediction models for the myriad environmental pollutants including those in automobile emissions. We chose 54 compounds related to engine exhaust and, by use of the DNA microarray, examined their effect on gene expression in human lung cells. We focused on IL-8 as a proinflammatory cytokine and developed a prediction model with quantitative structure-activity relationship (QSAR) for the IL-8 gene expression by using an in silico system. Our results demonstrate that this model showed high accuracy in predicting upregulation of the IL-8 gene. These results suggest that the prediction model with QSAR based on the gene expression from toxicogenomics may have great potential in predictive toxicology of environmental pollutants.

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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