Modeling and prediction of amino acids lipophylicity using multiple linear regression coupled with genetic algorithm

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.

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Principles of QSAR Modeling

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.20200701.oa1 ◽

2020 ◽

Vol 5 (3) ◽

pp. 61-97 ◽

Cited By ~ 7

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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Combined Genetic Algorithm and Multiple Linear Regression (GA-MLR) Optimizer: Application to Multi-exponential Fluorescence Decay Surface

The Journal of Physical Chemistry A ◽

10.1021/jp063998e ◽

2006 ◽

Vol 110 (48) ◽

pp. 12977-12985 ◽

Cited By ~ 8

Author(s):

Jacek J. Fisz

Keyword(s):

Genetic Algorithm ◽

Linear Regression ◽

Multiple Linear Regression ◽

Fluorescence Decay

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Perbandingan Model Estimasi Artificial Neural Network Optimasi Genetic Algorithm dan Regresi Linier Berganda

MEDIA STATISTIKA ◽

10.14710/medstat.10.1.13-23 ◽

2017 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Jimmy Saputra Sebayang ◽

Budi Yuniarto

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Linear Regression ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Least Square ◽

Simulation Data ◽

Ordinary Least Square

Multiple Linear Regression is a statistical approach most commonly used in performing predictive data modeling. One of the methods that can be used in estimating the parameters of the model on Multiple Linear Regression is Ordinary Least Square. It has classical assumptions requirements and often the assumptions are not satisfied. Another method that can be used as an alternative data modeling is Artificial Neural Network. It is a free-distribution estimator because there's no assumptions that have to be satisfied. However, modeling data using ANN has some problems such as selection of network topology, learning parameters and weight initialization. Genetic Algorithm method can be used to solve those problems. A set of simulation data was generated to test the reliability of ANN-GA model compared to Multiple Linear Regression model. Model comparison experiments indicate that ANN-GA model are better than Multiple Linear Regression model for estimating simulation data both on the data training and data testing.Keywords:Neural Network, Genetic Algorithm, Ordinary Least Square

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Development of scour around a circular pier and its modelling using genetic algorithm

Water Science & Technology Water Supply ◽

10.2166/ws.2020.244 ◽

2020 ◽

Vol 20 (8) ◽

pp. 3358-3367

Author(s):

Manish Pandey ◽

Mohammad Zakwan ◽

Mohammad Amir Khan ◽

Swati Bhave

Keyword(s):

Experimental Data ◽

Genetic Algorithm ◽

Linear Regression ◽

Multiple Linear Regression ◽

Bridge Pier ◽

Scour Depth ◽

Clear Water ◽

Depth Prediction ◽

Depth Relationship

Abstract This paper deals with generalized scour estimation to investigate maximum scour depth at equilibrium scour condition using experimental data obtained from experiments conducted by the authors along with data of previous researchers. Three hundred experimental data were used to derive the generalized clear water scour relationship around circular a bridge pier by using genetic algorithm (GA) and multiple linear regression (MLR) techniques. The GA-based maximum scour depth relationship showed more precise results than MLR. In addition, the present GA and MLR relationships were compared with some equations developed by earlier researchers. Graphically and statistically, it was observed that the GA and MLR relationships provide better agreement with experimental data as compared to earlier relationships. The present study highlights that the GA approach could be effectively used for estimation of maximum scour depth prediction around the bridge pier.

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