Quantitative structural–activity relationship (QSAR) study for fungicidal activities of thiazoline derivatives against rice blast

2008 ◽  
Vol 18 (6) ◽  
pp. 2133-2142 ◽  
Author(s):  
Jin Soo Song ◽  
Taesung Moon ◽  
Kee Dal Nam ◽  
Jae Kyun Lee ◽  
Hoh-Gyu Hahn ◽  
...  
Keyword(s):  
Rice Blast ◽  
Activity Relationship ◽  
Qsar Study ◽  
Structural Activity Relationship ◽  
Quantitative Structural Activity Relationship

Quantitative structural activity relationship study of bis-tetraazacyclic compounds. A novel series of HIV-1 and HIV-2 inhibitors

1995 ◽  
Vol 38 (19) ◽  
pp. 3865-3873 ◽  
Author(s):  
Heidi C. Joao ◽  
Karen De Vreese ◽  
Rudi Pauwels ◽  
Erik De Clercq ◽  
Geoff W. Henson ◽  
...  
Keyword(s):  
Activity Relationship ◽  
Structural Activity Relationship ◽  
Relationship Study ◽  
Quantitative Structural Activity Relationship ◽  
Hiv 1

Use of Graph Based Descriptors for Determination of Structural Features Causing Modulation of Fructose-1,6-Bisphosphatase

Drug Research ◽  
2020 ◽  
Vol 70 (05) ◽  
pp. 226-232 ◽  
Author(s):  
Ashwani Kumar ◽  
Kiran Bagri ◽  
Parvin Kumar ◽  
Keyword(s):  
Research Work ◽  
Activity Relationship ◽  
Training Set ◽  
Molecular Docking Study ◽  
Statistical Parameters ◽  
Monte Carlo Optimization ◽  
Fructose 1,6 Bisphosphatase ◽  
Structural Activity Relationship ◽  
Quantitative Structural Activity Relationship

AbstractFructose-1,6-bisphosphatase performs a significant function in regulating the blood glucose level in type 2 diabetes by controlling the process gluconeogenesis. In this research work optimal descriptor (graph) based quantitative structural activity relationship studies of a set of 203 fructose-1,6-bisphosphatase has been performed with the help of Monte Carlo optimization. Distribution of compounds into different sets such as training set, invisible training set, calibration set and validation sets resulted in formation of splits. Statistical parameters obtained from quantitative structural activity relationship modeling were good for various designed splits. The statistical parameters such as R2 and Q2 for calibration and validation sets of best split developed were found to be 0.8338, 0.7908 & 0.7920 and 0.7036 respectively. Based on the results obtained for correlation weights, different structural attributes were described as promoters and demoters of the endpoint. Further these structural attributes were used in designing of new fructose-1,6-bisphosphatase inhibitors and molecular docking study was accomplished for the determination of interactions of designed molecules with the enzyme.

scholarly journals Recent Trends in the Development of Benzimidazole Hybrid Derivatives and Their Antimalarial Activities

2021 ◽  
pp. 44-53
Author(s):  
Waikhom Somraj Singh ◽  
Bikash Debnath ◽  
Kuntal Manna
Keyword(s):  
Antimalarial Activity ◽  
Plasmodium Species ◽  
Qsar Model ◽  
Infected Mosquito ◽  
Activity Relationship ◽  
Benzimidazole Derivatives ◽  
Wide Range ◽  
Qsar Models ◽  
Structural Activity Relationship ◽  
Quantitative Structural Activity Relationship

A parasite of the Plasmodium species initiates malaria. The parasite is transmitted to communities through the bite of an infected mosquito. Malarial resistance towards the commonly used antimalarial agents is a genuine human health problem. Benzimidazole derivatives exhibit a wide range of antimalarial activities against Plasmodium falciparum (P. falciparum) strain. The present review has summarized the antimalarial activity of benzimidazole hybrid derivatives and described its structural activity relationship (SAR) and quantitative structural-activity relationship (QSAR) model. A total of 14 papers were systematically reviewed. The literature survey has revealed that novel benzimidazole hybrid derivatives diminished the P. falciparum activity in the liver and gametocyte stages and inhibited heme synthesis and β-hematin formation. The QSAR models explain imminent antimalarial agent's growth through multiple linear regression (MLR) and artificial neural networks (ANN).

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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