In silico Exploration of the Therapeutic Potential of Phytocannabinoids.

Nour H.; Mounadi N.; Chtita S. · Mini reviews in medicinal chemistry · 2026

**Background & Methods** This is a narrative review synthesizing in silico (computational) studies published from 2023-2026 that employed molecular docking, molecular dynamics (MD) simulations, and ADMET (absorption, distribution, metabolism, excretion, toxicity) analyses to investigate phytocannabinoid interactions with molecular targets. The review examines how plant-derived cannabinoids bind to disease-relevant proteins across neurodegenerative, metabolic, oncological, and viral disease pathways. **Key Findings** - Multiple phytocannabinoids (Δ9-THC, CBN, THCV, THCA) demonstrated predicted binding affinity to numerous disease-associated proteins including acetylcholinesterase, butyrylcholinesterase, EGFR, BRAF/TGF-β, α-amylase, and SARS-CoV-2 main protease (Mpro), suggesting potential therapeutic applications across multiple disease categories. - Computational analyses revealed stable molecular dynamics trajectories and favorable ADMET predictions for studied phytocannabinoids, providing in silico evidence of potential bioavailability and safety profiles. - Significant methodological limitations were identified: docking scores from different computational protocols lack direct comparability, and computational predictions do not guarantee actual biological efficacy or clinical safety without experimental validation. **Dosage & Administration** Not reported. This review focuses on computational predictions rather than clinical or experimental studies with defined dosing regimens. **Safety & Adverse Effects** Not reported. The review does not present experimental or clinical safety data. While ADMET analyses predicted favorable safety profiles computationally, the authors emphasize that these predictions do not guarantee actual biological safety without experimental validation. **Evidence Quality** This review represents low-to-moderate evidence quality as a synthesis of in silico studies. Critical limitations include: (1) computational docking and MD simulations provide theoretical binding predictions only, lacking biological validation; (2) inconsistent methodologies across studies prevent direct comparison of docking scores; (3) ADMET predictions are surrogate markers that do not confirm pharmacokinetic or toxicological outcomes in biological systems; (4) no experimental or clinical data are presented. The authors appropriately highlight methodological gaps and call for integration of computational approaches with rigorous experimental validation including binding assays, cellular studies, and clinical trials before claims of therapeutic efficacy can be substantiated.

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