Loading...
Please wait, while we are loading the content...
Similar Documents
Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy
| Content Provider | MDPI |
|---|---|
| Author | Wang, Mukuo Hou, Shujing Liu, Ye Li, Dongmei Lin, Jianping |
| Copyright Year | 2021 |
| Description | The endocannabinoid system plays an essential role in the regulation of analgesia and human immunity, and Cannabinoid Receptor 2 (CB2) has been proved to be an ideal target for the treatment of liver diseases and some cancers. In this study, we identified CB2 antagonists using a three-step “deep learning–pharmacophore–molecular docking” virtual screening approach. From the ChemDiv database (1,178,506 compounds), 15 hits were selected and tested by radioligand binding assays and cAMP functional assays. A total of 7 out of the 15 hits were found to exhibit binding affinities in the radioligand binding assays against CB2 receptor, with a $pK_{i}$ of 5.15-6.66, among which five compounds showed antagonistic activities with $pIC_{50}$ of 5.25–6.93 in the cAMP functional assays. Among these hits, Compound 8 with the 4H-pyrido[1,2-a]pyrimidin-4-one scaffold showed the best binding affinity and antagonistic activity with a $pK_{i}$ of 6.66 and $pIC_{50}$ of 6.93, respectively. The new scaffold could serve as a lead for further development of CB2 drugs. Additionally, we hope that the model in this study could be further utilized to identify more novel CB2 receptor antagonists, and the developed approach could also be used to design potent ligands for other therapeutic targets. |
| Starting Page | 6679 |
| e-ISSN | 14203049 |
| DOI | 10.3390/molecules26216679 |
| Journal | Molecules |
| Issue Number | 21 |
| Volume Number | 26 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-04 |
| Access Restriction | Open |
| Subject Keyword | Molecules Medicinal Chemistry Cb2 Receptor Antagonist Deep Learning Pharmacophore Molecular Docking Multi-step Virtual Screening |
| Content Type | Text |
| Resource Type | Article |
