Leveraging machine learning for selective cannabinoid ligand discovery: methods, challenges, and opportunities.

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• Selective modulation of cannabinoid receptors (CB2 over CB1) offers safer therapeutics with fewer psychotropic effects.
• Machine learning (ML) presents a powerful approach to overcome challenges in cannabinoid receptor selectivity and drug discovery.

Purpose of the Study:

• To review current ML methodologies for cannabinoid ligand discovery, focusing on predicting receptor affinity and selectivity.
• To highlight advances in deep learning and generative models for de novo molecular design in this field.

Main Methods:

• Literature search on PubMed followed by manual screening for AI-driven cannabinoid ligand discovery studies.
• Discussion of feature engineering (molecular fingerprints, physicochemical descriptors, SMILES) and ML algorithms (classification, regression).
• Evaluation of model performance, dataset limitations, and interpretability challenges.

Main Results:

• ML has significantly improved the prediction of cannabinoid receptor selectivity.
• Deep learning and generative models show potential for expanding chemical space and identifying selective ligands.
• Current progress is limited by data quality, endpoint inconsistency, and model interpretability.

Conclusions:

• Future research integrating curated datasets, mechanistic modeling, and generative AI is crucial.
• These advancements are expected to significantly enhance the discovery of selective cannabinoid therapeutics.
• Addressing data quality and interpretability will be key to realizing the full potential of ML in this area.