Abstract
CB2R are fascinating targets for neuropathic pain and mood disorders because of their improved biological characteristics. Experimental data on 1296 cannabinoid-2 receptor inhibitors with different structural properties were used to develop a QSAR model following OECD guidelines. This study selected the best-predicted model (80:20 splitting ratio) with fitting parameters, such as R2 :0.78; F:623.6, Internal validation parameters, such as Q2 Loo :0.78; CCCcv: 0.87 and external validation parameters, such as R2 ext :0.77; Q2 F1 :0.7730; Q2 F2 :0.7730; Q2 F3 :0.76; CCCext :0.87. Following this, another QSAR model was developed by using a 50:50 split ratio for thetraining and the prediction sets, which were then swapped to evaluate the robustness of the built QSAR model by the 50:50 ratio, which also gives a deeper understanding of the chemical space. In addition, we have confirmed the QSAR result with pharmacophore modelling, and supported by molecular docking, MD simulation, MMGBSA and ADME studies. Thus, this work may enable cannabinoid 2 receptor inhibsitor development.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
