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Demonstrates enhancements in cannabinoid ligand discovery using machine learning, suggesting avenues for therapeutic development.
ML has significantly advanced the prediction of cannabinoid receptor selectivity, yet progress remains constrained by data quality, endpoint inconsistency, and limited interpretability. Future efforts integrating curated datasets, mechanistically informed modeling, and generative AI frameworks are expected to substantially enhance the discovery of selective cannabinoid therapeutics.
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Liu et al. (2026) studied this question.
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