Comprehensive 2D-Quantitative Structure-Activity Relationship Study on Monobactam Analogues Against Gram-Negative Bacteria

2020 ◽  
Vol 16 (6) ◽  
pp. 941-953
Author(s):  
Dousheng Zhang ◽  
Xia Zhang ◽  
Zhiwen Li ◽  
Sheng Tang ◽  
Zhihao Guo ◽  
...  
Keyword(s):  
Bacterial Infections ◽  
Rational Design ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Klebsiella Pneumonia ◽  
Gram Negative Bacteria ◽  
Quantitative Structure ◽  
Gram Negative ◽  
Structure Activity

Human health has been severely affected by infections resulting from multidrug-resistant (MDR) gram-negative bacteria (GNB). Monobactam antibiotics are known to be effective against such infections. This study aimed to construct a predictive two-dimensional quantitative structure-activity relationship (2D-QSAR) model for the rational design of new monobactams based on the 65 known monobactams against Escherichia coli (Eco) and Klebsiella pneumonia (Kpn) strains using the kernel partial least squares regression (KPLS) algorithm. The total performance of Eco and Kpn KPLS modes was shown as RMSE: 0.681/0.596, R2: 0.946/0.882, Q2: 0.922/0.877, and RMSU: 0.625/0.593. Thirty-four monobactams reported in our lab were chosen as external data to predict their activities against Eco and Kpn using the newly established models, by which the R2 between the experimental and predicted values was 0.878 and 0.871, respectively. The models developed and verified in this study provide a powerful design strategy for novel monobactams that are effective against MDR gram-negative bacterial infections.

scholarly journals Rational Design of Novel Anti-microtubule Agent (9-Azido-Noscapine) from Quantitative Structure Activity Relationship (QSAR) Evaluation of Noscapinoids

2011 ◽  
Vol 16 (9) ◽  
pp. 1047-1058 ◽  
Author(s):  
Seneha Santoshi ◽  
Pradeep K. Naik ◽  
Harish C. Joshi
Keyword(s):  
Rational Design ◽  
Lymphoblastic Leukemia ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Nuclear Magnetic Resonance Analysis ◽  
Quantitative Structure ◽  
Structural Framework ◽  
Structure Activity

An anticough medicine, noscapine [(S)-3-((R)4-methoxy-6-methyl-5,6,7,8-tetrahydro-[1,3]dioxolo[4,5-g]isoquinolin-5-yl)-6,7-dimethoxyiso-benzofuran-1(3H)-one], was discovered in the authors’ laboratory as a novel type of tubulin-binding agent that mitigates polymerization dynamics of microtubule polymers without changing overall subunit-polymer equilibrium. To obtain systematic insight into the relationship between the structural framework of noscapine scaffold and its antitumor activity, the authors synthesized strategic derivatives (including two new ones in this article). The IC50 values of these analogs vary from 1.2 to 56.0 µM in human acute lymphoblastic leukemia cells (CEM). Geometrical optimization was performed using semiempirical quantum chemical calculations at the 3-21G* level. Structures were in agreement with nuclear magnetic resonance analysis of molecular flexibility in solution and crystal structures. A genetic function approximation algorithm of variable selection was used to generate the quantitative structure activity relationship (QSAR) model. The robustness of the QSAR model ( R2 = 0.942) was analyzed by values of the internal cross-validated regression coefficient ( R2LOO = 0.815) for the training set and determination coefficient ( R2test = 0.817) for the test set. Validation was achieved by rational design of further novel and potent antitumor noscapinoid, 9-azido-noscapine, and reduced 9-azido-noscapine. The experimentally determined value of pIC50 for both the compounds (5.585 M) turned out to be very close to predicted pIC50 (5.731 and 5.710 M).

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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