QSPR analysis for melting point of fatty acids using genetic algorithm based multiple linear regression (GA-MLR)

2013 ◽  
Vol 353 ◽  
pp. 15-21 ◽  
Author(s):  
Guijie Liang ◽  
Jie Xu ◽  
Li Liu
Keyword(s):  
Genetic Algorithm ◽  
Fatty Acids ◽  
Melting Point ◽  
Linear Regression ◽  
Multiple Linear Regression

scholarly journals Digestion by pigs of non-starch polysaccharides in wheat and raw peas (Pisum sativum) fed in mixed diets

1991 ◽  
Vol 65 (2) ◽  
pp. 259-270 ◽  
Author(s):  
J. S. Goodlad ◽  
J. C. Mathers
Keyword(s):  
Fatty Acids ◽  
Linear Regression ◽  
Pisum Sativum ◽  
Multiple Linear Regression ◽  
Volatile Fatty Acids ◽  
Bacterial Biomass ◽  
Diaminopimelic Acid ◽  
Inclusion Rate ◽  
Linear Regression Technique ◽  
Stimulation Of

The digestion by pigs of non-starch polysaccharides (NSP) in wheat and raw peas (Pisum sativum) fed in mixed diets was measured. In the four experimental diets, wheat was included at a constant 500 g/kg whilst peas contributed 0–300 g/kg and these were the only dietary sources of NSP. Separate estimates of digestibility for wheat and peas were obtained by using a multiple linear regression technique which also tested the possibility that the presence of peas might influence the digestibility of wheat NSP. There was little evidence of the latter and it was found that the digestibility of peas NSP (0.84) was considerably greater than that of wheat (0.65). The non-cellulosic polysaccharides (NCP) had twofold greater digestibilities than had cellulose for both foods with essentially all the peas NCP being digested. Faecal α-diaminopimelic acid concentration increased with feeding of peas, suggesting stimulation of bacterial biomass production in the large intestine using the readily fermented peas NSP. All three major volatile fatty acids produced by large intestinal fermentation were detected in jugular blood and increased significantly with increasing peas inclusion rate in the diet.

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

2013 ◽  
Vol 23 (5) ◽  
pp. 2264-2276 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Reza Aalizadeh ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi ◽  
Javad Shadmanesh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Qsar Study ◽  
Multiple Linear Regression Method

Prediction of PCE of fullerene (C 60 ) derivatives as polymer solar cell acceptors by genetic algorithm–multiple linear regression

2015 ◽  
Vol 21 ◽  
pp. 1058-1067 ◽  
Author(s):  
Eslam Pourbasheer ◽  
Alireza Banaei ◽  
Reza Aalizadeh ◽  
Mohammad Reza Ganjali ◽  
Parviz Norouzi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Solar Cell ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Polymer Solar Cell

scholarly journals Quantitative Structure–Activity Relationship and Molecular Docking Studies of Imidazolopyrimidine Amides as Potent Dipeptidyl Peptidase-4 (DPP4) Inhibitors

2019 ◽  
pp. 1-15 ◽  
Author(s):  
Leila Emami ◽  
Razieh Sabet ◽  
Amirhossein Sakhteman ◽  
Mehdi Khoshnevis Zade
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Quantitative Structure Activity Relationship ◽  
Dipeptidyl Peptidase ◽  
Docking Studies ◽  
Quantitative Structure ◽  
Dipeptidyl Peptidase 4 ◽  
Structure Activity

Type 2 diabetes (T2DM) is a metabolic disorder disease and DPP-4 inhibitors are a class of oral hypoglycemic that blocks the dipeptidyl peptidase-4 (DPP-4) enzyme.  DPP-4 inhibitors reduce glucagon and blood glucose levels and don’t have side effects such as hypoglycemia or weight gain. In this paper, a series of imidazolopyrimidine amides analogues as DPP4 inhibitors were selected for quantitative structure-activity relationship (QSAR) analysis and docking studies. A collection of chemometric methods such as multiple linear regression (MLR), factor analysis-based multiple linear regression (FA-MLR), principal component regression (PCR), genetic algorithm for variable selection-MLR (GA-MLR) and partial least squared combined with genetic algorithm for variable selection (GA-PLS), were conducted to make relations between structural features and DPP4 inhibitory of a variety of imidazolopyrimidine amides derivatives. GA-PLS represented superior results with high statistical quality (R2 = 0.94 and Q2 = 0.80) for predicting the activity of the compounds. Docking studies of these compounds reveals and confirms that compounds 15, 18, 25, 26, and 28 are introduced as good candidates for DPP-4 inhibitors were introduced as a good candidate for DPP-4 inhibitory compounds.

scholarly journals Principles of QSAR Modeling

2020 ◽  
Vol 5 (3) ◽  
pp. 61-97 ◽  
Author(s):  
Paola Gramatica
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Variable Selection ◽  
Multiple Linear Regression ◽  
Molecular Descriptors ◽  
Academic Career ◽  
Environmental Pollutants ◽  
Ordinary Least Squares ◽  
Qsar Modeling ◽  
Qsar Models

At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.

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