Literature DB >> 26795018

Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

Alexander Paulke1, Ewgenij Proschak2, Kai Sommer2, Janosch Achenbach2, Cora Wunder3, Stefan W Toennes3.   

Abstract

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  In-silico prediction model; QSAR; Synthetic cannabinoids

Mesh:

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Year:  2016        PMID: 26795018     DOI: 10.1016/j.toxlet.2016.01.001

Source DB:  PubMed          Journal:  Toxicol Lett        ISSN: 0378-4274            Impact factor:   4.372


  3 in total

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Journal:  Mikrochim Acta       Date:  2022-08-04       Impact factor: 6.408

2.  Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2.

Authors:  Mikołaj Mizera; Dorota Latek; Judyta Cielecka-Piontek
Journal:  Int J Mol Sci       Date:  2020-07-26       Impact factor: 5.923

3.  QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids.

Authors:  Wonyoung Lee; So-Jung Park; Ji-Young Hwang; Kwang-Hyun Hur; Yong Sup Lee; Jongmin Kim; Xiaodi Zhao; Aekyung Park; Kyung Hoon Min; Choon-Gon Jang; Hyun-Ju Park
Journal:  Molecules       Date:  2020-12-21       Impact factor: 4.411

  3 in total

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