Quantum chemical descriptors in quantitative structure–activity relationship models and their applications

2021 ◽  
pp. 104384
Author(s):  
Liangliang Wang ◽  
Junjie Ding ◽  
Li Pan ◽  
Dongsheng Cao ◽  
Hui Jiang ◽  
...  
Keyword(s):  
Quantum Chemical ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Quantum Chemical Descriptors ◽  
Structure Activity ◽  
Chemical Descriptors

scholarly journals QUANTUM CHEMICAL STUDY FOR THE TOXICITY PREDICTION OF SULFONAMIDE ANTIBIOTICS WITH QUANTITATIVE STRUCTURE – ACTIVITY RELATIONSHIP

2020 ◽  
Vol 51 (1) ◽  
pp. 7-13
Author(s):  
S. Aydogdu ◽  
Arzu Hatipoglu
Keyword(s):  
Correlation Coefficient ◽  
Quantum Chemical ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Quantum Chemical Descriptors ◽  
Structure Activity ◽  
Qsar Models ◽  
Chemical Descriptors

Sulfonamides are one of the most important classes of chemicals found in the aquatic environment as a pollutant due to excessive consumption. The DFT- B3LYP method with the basis set 6-311++G (d,p) was employed to calculate various quantum chemical descriptors of sulfonamide molecules. A quantitative structure activity relationship (QSAR) study was performed for the toxicity value LD50 of sulfonamides with their quantum chemical descriptors by multi linear regression. The QSAR models were validated by internally and externally. The best multilinear equation with correlation coefficient, R and the cross-validation leave-one-out correlation coefficient, Q2 values were 0.9528 ,0.8556 respectively The results show that the QSAR models have both favourable estimation stability and good prediction power.

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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