Scoring Functions and Modeling of Structure-Activity Relationships for Cannabinoid Receptors

Sapundzhi, Fatima

doi:10.3991/ijoe.v15i11.10893

Cited by 10 publications

(7 citation statements)

References 17 publications

(21 reference statements)

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“…From the relationship found between the docking results and the in vivo test, we could predict the biological effect of newly synthesized analogues, which is much higher than other compounds in the tested series. For some work along these lines, see [38][39][40][41][42][43][44][45][46][47][48].…”

Section: C)mentioning

confidence: 99%

A Study of QSAR based on Polynomial Modeling in Matlab

Sapundzhi

Dzimbova

2019

Int. J. Onl. Eng.

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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.

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Section: C)mentioning

confidence: 99%

A Study of QSAR based on Polynomial Modeling in Matlab

Sapundzhi

Dzimbova

2019

Int. J. Onl. Eng.

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“…The next step is to consider the protein as rigid or flexible and choose a structure alignment method, which may consist of a scoring function to measure protein similarity and a search algorithm to optimize the function [8][9]. For more details of scoring function see Sapundzhi et al [10][11]. An important task for researchers is to evaluate the similarity of two protein structures.…”

Section: Introductionmentioning

confidence: 99%