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Predicting affinity and potency of new psychoactive substances at cannabinoid 1 receptor with explainable artificial
Verena Schöning1, Gaia Alluisetti2,3, Katharina Elisabeth Grafinger2
1Clinical Pharmacology and Toxicology, Department of Internal Medicine, University Hospital Bern, Bern, Switzerland.
Frontiers in Pharmacology
|June 5, 2026
Summary
Machine learning models accurately predict cannabinoid receptor 1 (CB1) affinity and potency for new psychoactive substances (NPS). Lipophilicity drives affinity, while potency involves a wider range of molecular features, aiding in the regulation of recreational drugs.
Area of Science:
- Pharmacology and Cheminformatics
- Drug Discovery and Regulation
Background:
- Binding affinity and functional potency are key ligand-receptor interaction properties.
- Understanding molecular features is crucial for regulating new psychoactive substances (NPS), like synthetic cannabinoid receptor agonists.
- Synthetic cannabinoid receptor agonists are often full agonists of the cannabinoid 1 receptor (CB1).
Purpose of the Study:
- To develop machine learning models predicting CB1 receptor affinity and potency.
- To identify molecular features driving these interactions in NPS.
- To aid in the regulation of recreational drugs by understanding structure-activity relationships.
Main Methods:
- Compiled publicly available data on CB1 affinity and potency.
- Employed molecular descriptors and fingerprints for machine learning model training.
- Utilized explainable AI (SHAP values) to analyze feature importance for affinity and potency.
Main Results:
- XGBoost and Random Forest models achieved >90% recall, precision, and F1 scores for predicting both affinity and potency.
- Models utilizing molecular descriptors and Extended Connectivity Fingerprints (ECFP) showed superior performance.
- SHAP analysis highlighted key structural features influencing CB1 affinity and potency in NPS.
Conclusions:
- Lipophilicity and membrane-partitioning are primary drivers of CB1 receptor affinity.
- Potency is influenced by a combination of lipophilicity, shape, branching, and electronic descriptors.
- These findings provide valuable insights for the design and regulation of synthetic cannabinoids and other NPS.
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