Virtual Reverse Screening Approach to Target Type 2 Cannabinoid Receptor.

Area of Science:

• Medicinal Chemistry
• Computational Drug Discovery
• Pharmacology

Background:

• The type 2 cannabinoid receptor (CB2) is a key target in various physiological processes.
• Identifying selective ligands for CB2 is crucial for therapeutic development.
• Existing drug-like molecule databases offer a rich source for novel ligand discovery.

Purpose of the Study:

• To perform a large-scale ligand-based reverse screening for CB2 receptor activity.
• To identify high-confidence drug-like molecules predicted to be active against CB2.
• To explore potential drug repurposing opportunities based on CB2 target engagement.

Main Methods:

• Utilized a screening pool of 617,710 drug-like molecules from the ChEMBL database.
• Employed the PLATO polypharmacological web platform for computational screening.
• Applied predicted bioactivity values and probability thresholds for molecule prioritization.

Main Results:

• Prioritized 233 out of 617,710 molecules with predicted bioactivity > 0.2 μM and ~98% probability against CB2.
• Successfully identified a subset of drug-like molecules with high predicted affinity for the CB2 receptor.
• Initiated investigation into potential CB2-related targets for drug repurposing.

Conclusions:

• The PLATO platform effectively identifies promising CB2 ligands from large chemical libraries.
• The prioritized molecules represent valuable starting points for developing novel CB2-targeting therapeutics.
• This study lays the groundwork for future drug repurposing initiatives targeting the CB2 receptor.