Homology Modelling and Evaluation of the CannabinoidReceptor Type 2

Sapundzhi, Fatima; Dzimbova, Tatyana; Pencheva, Nevena; Milanov, Peter

doi:10.17952/35eps.2018.327

Cited by 2 publications

(10 citation statements)

References 5 publications

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“…The docking procedure was performed by using the software for molecular docking GOLD and the ChemScore algorithms, see [11,13,14]. The scoring results give information on how good the pose is.…”

Section: Docking Proceduresmentioning

confidence: 99%

“…Molecular docking is one of the fundamentals of CADD that analyzes the binding interaction between the target and the small molecules called ligands. These ligands are potential drug candidates for the development of phenotypic and therapeutic models targeted by the CADD proteins [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [11] we have investigated the relationship between the values of the biological activity of delta-opioid analogues that were previously synthesized and biologically tested and have made some investigations over the results of the in silico docking. Moreover, the calculation of the minimal energy conformation for each obtained ligand-receptor complex after the docking procedure was investigated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Sapundzhi,

Lazarova,

Dzimbova

et al. 2023

J. Phys.: Conf. Ser.

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Opiates are among the oldest drugs that are used to treat many medical problems. They are analgesic and sedative drugs that contain opium. The morphine is its most active ingredient and it is a widely used pain reliever despite its side effects. The main objective of this study is to construct a model which gives the structure-activity relationship among a series of mu-opioid ligands and molecular docking results. For this purpose, a model of mu-opioid receptors using machine learning is introduced. By obtaining a relationship between the docking results and the in vivo test, we could predict the biological effect of the newly synthesized ligands.

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Section: Docking Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sapundzhi,

Lazarova,

Dzimbova

et al. 2023

J. Phys.: Conf. Ser.

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“…These data show that the best correlation concerns the model of CB1 receptor obtained by MOE and the scoring function ChemScore, because the value of the Pearson's coefficient is highest and the relationship is negative. For the CB1 model, generated by Chimera and the same scoring function, the correlation coefficient was similar [25][26][27][28]. Table 2.…”

mentioning

confidence: 99%

“…In our previous studies it was generated a 3D model of Delta opioid receptor (DOR) by using homology modelling [27]. Newly generated model of DOR was used for in silico studies and it was compared with recently published data for crystal structure of DOR [28][29][30][31]. It was established that the most appropriate combination for analysis of investigated compounds and DOR is that between the model of DOR obtained by homology modelling and the ASP scoring function (available in GOLD 5.2) [31][32][33][34].…”

mentioning

confidence: 99%

Computer modeling of Cannabinoid receptor type 1

et al. 2018

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Cannabinoid receptors are important class of receptors as they are involved in various physiological processes such as appetite, pain-sensation, mood, and memory. It is important to design receptor-selective ligands in order to treat a particular disorder. The aim of the present study is to model the structure of cannabinoid receptor CB1 and to perform docking between obtained models and known ligands. Two models of CBR1 were prepared with two different methods (Modeller of Chimera and MOE). They were used for docking with GOLD 5.2. It was established a high correlation between inhibitory constant Ki of CB1 cannabinoid ligands and the ChemScore scoring function of GOLD, which concerns both models. This suggests that the models of the CB1 receptors obtained could be used for docking studies and in further investigation and design of new potential, selective and active cannabinoids with the desired effects.

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Homology Modelling and Evaluation of the CannabinoidReceptor Type 2

Cited by 2 publications

References 5 publications

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

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

Computer modeling of Cannabinoid receptor type 1

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