A Study of QSAR based on Polynomial Modeling in Matlab

Abstract

Mu-opioid receptor (MOR) is an attractive target for <em>in silico</em> docking experiments. Many potent analgesics currently in use act through the MOR. The main objective of the present work was to find the polynomial function for modelling of the structure-activity relationship of a series of MOR analogues and the results of the molecular docking with MOR (PDBid:4dkl). The relationship of the biological activity of the ligands with the ChemScore function and with the total energy (MolDock function) was modelled with first- to third-degree polynomials and surface fitted method, assessed by least squares method. The finding, established in the paper, suggests that the third order polynomial could be successfully used for modelling of the relationship between the biological effect of the MOR analogues and results from docking procedure. Analysis and comparison of the data from in vitro tests and docking studies could help to understand better the relationship between in vitro biological effects and docking studies and to answer whether the models of the biological macromolecules (in our case MOR) correspond to the real 3D structure.

On Analysis of Banhatti Indices for Hyaluronic Acid Curcumin and Hydroxychloroquine

“…He observed a relation between the boiling point of paraffin and the Wiener index. e Randic index, Zagreb index, and the Szeged index [2][3][4] are some of the most well-known topological indices used to investigate the quantitative structure-activity (QSAR) and quantitative structureproperty (QSPR) relationships of chemical graphs and nanostructures [5][6][7][8][9][10].…”

An application of some machine learning methods for biological data modeling

“…It is suitable for determining the relationship structure and biological activity (𝑅 2 = 1.0, 𝑆𝑆𝐸 = 0.0092, 𝑎𝑑𝑗 𝑅 2 = 0.9999, 𝑅𝑀𝑆𝐸 = 0.0960). Similar studies had been conducted with other compounds, see [13][14][15][16].…”

“…In these methods, small molecules are matched to select compounds for further study based on their properties. In the early stages of drug discovery, the molecules are evaluated based on some characteristics such as similarity, correlation with specific properties or binding energy [10][11][12][13][14][15][16].…”

A structure-activity relationship modelling of opioid compounds by using machine learning

“…and was suitable for determining the structure-biological activity relationship: 𝑅 2 = 1.0 𝑆𝑆𝐸 = 0.6290, 𝑎𝑑𝑗 𝑅 2 = 0.9999, 𝑅𝑀𝑆𝐸 = 0.5608. Similar studies have been conducted with other compounds, see [11,[18][19][20][21].…”

“…In our previous research, see [11,[18][19][20] the experimental data fitting for DOR was carried out by using a polynomial function 𝑧 = 𝑓 (𝑥, 𝑦) where the values (𝑧 1 , 𝑧 2 , . .…”