Computationally Guided Discovery of Cannabinoid Derivatives for Antiviral and Anticancer Applications: From Chemical Space to Experimental Validation
Abstract
Cannabinoids represent a chemically diverse and structurally tunable class of natural and synthetic compounds that have attracted growing interest as molecular scaffolds for therapeutic discovery. However, their extensive chemical diversity, polypharmacology, conformational flexibility, and complex pharmacokinetic properties make the systematic identification and optimisation of therapeutically useful derivatives challenging. Recent advances in computer-aided drug discovery (CADD), molecular simulation, quantum chemistry, quantitative structure–activity relationship (QSAR) modelling, and artificial intelligence (AI) provide opportunities to interrogate the chemical space of cannabinoids in a systematic and mechanistically informed manner. This review examines computational strategies for the discovery and optimisation of cannabinoid derivatives with potential antiviral and anticancer applications, emphasising the integration of complementary computational approaches rather than reliance on isolated in silico predictions. A hierarchical computational workflow is described, progressing from cannabinoid chemical-space generation and ligand preparation to disease-relevant target identification, virtual screening, molecular docking, molecular dynamics (MD) simulations, binding free-energy estimation, quantum-chemical analysis, and ADMET/drug-likeness prediction. For antiviral discovery, computational investigations of SARS-CoV-2 targets, including the main protease (Mpro/3CLpro), papain-like protease (PLpro), spike protein, and spike–ACE2 interface, are discussed alongside emerging host-directed targets. For anticancer applications, computational studies involving EGFR, HER2, VEGFR, PI3K/AKT, MAPK, apoptosis, autophagy, oxidative stress, and cell-cycle regulation are evaluated. Particular emphasis is placed on the complementary roles of docking and MD simulations in characterising binding modes, interaction persistence, conformational stability, and molecular recognition, while MM-PBSA/MM-GBSA and related approaches provide additional energetic criteria for candidate prioritisation. QSAR and AI/ML-assisted approaches are further considered as complementary tools for expanding chemical-space exploration, predicting biological activity and ADMET properties, and enabling multi-parameter candidate prioritisation. Importantly, computational affinity scores are not treated as sufficient evidence of target inhibition or therapeutic efficacy. Instead, the review emphasises a progressive validation framework in which computationally prioritised candidates are evaluated through biochemical target-engagement studies, cellular assays, and, where appropriate, in vivo models. Experimental observations can subsequently inform structure–activity relationship analysis, lead optimisation, and iterative computational rescreening, establishing a closed-loop strategy for integrating molecular recognition, dynamic behaviour, binding energetics, pharmacokinetic properties, toxicity, and biological activity during cannabinoid lead development. The review critically examines key limitations of current computational approaches, including incomplete treatment of ligand flexibility, scoring-function limitations, protein conformational heterogeneity, limited experimental validation, dataset bias in AI/ML models, and translational challenges associated with cannabinoid solubility, bioavailability, metabolism, receptor promiscuity, psychoactive liabilities, and off-target effects. Collectively, current evidence indicates that computationally guided cannabinoid discovery is a promising but predominantly preclinical strategy. Further progress will depend on integrating CADD, molecular simulation, AI-assisted molecular design, uncertainty-aware prediction, and experimental validation into iterative multi-objective optimisation frameworks that jointly address potency, selectivity, pharmacokinetics, toxicity, and translational relevance.
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BibTeX
@misc{yadav2026computationally,
title = {{Computationally Guided Discovery of Cannabinoid Derivatives for Antiviral and Anticancer Applications: From Chemical Space to Experimental Validation}},
author = {VIVEK KUMAR YADAV},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008399/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008399/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Computationally Guided Discovery of Cannabinoid Derivatives for Antiviral and Anticancer Applications: From Chemical Space to Experimental Validation
AU - VIVEK KUMAR YADAV
PY - 2026
DA - 2026/09/07
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008399/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008399/v1
AB - Cannabinoids represent a chemically diverse and structurally tunable class of natural and synthetic compounds that have attracted growing interest as molecular scaffolds for therapeutic discovery. However, their extensive chemical diversity, polypharmacology, conformational flexibility, and complex pharmacokinetic properties make the systematic identification and optimisation of therapeutically useful derivatives challenging. Recent advances in computer-aided drug discovery (CADD), molecular simulation, quantum chemistry, quantitative structure–activity relationship (QSAR) modelling, and artificial intelligence (AI) provide opportunities to interrogate the chemical space of cannabinoids in a systematic and mechanistically informed manner. This review examines computational strategies for the discovery and optimisation of cannabinoid derivatives with potential antiviral and anticancer applications, emphasising the integration of complementary computational approaches rather than reliance on isolated in silico predictions. A hierarchical computational workflow is described, progressing from cannabinoid chemical-space generation and ligand preparation to disease-relevant target identification, virtual screening, molecular docking, molecular dynamics (MD) simulations, binding free-energy estimation, quantum-chemical analysis, and ADMET/drug-likeness prediction. For antiviral discovery, computational investigations of SARS-CoV-2 targets, including the main protease (Mpro/3CLpro), papain-like protease (PLpro), spike protein, and spike–ACE2 interface, are discussed alongside emerging host-directed targets. For anticancer applications, computational studies involving EGFR, HER2, VEGFR, PI3K/AKT, MAPK, apoptosis, autophagy, oxidative stress, and cell-cycle regulation are evaluated. Particular emphasis is placed on the complementary roles of docking and MD simulations in characterising binding modes, interaction persistence, conformational stability, and molecular recognition, while MM-PBSA/MM-GBSA and related approaches provide additional energetic criteria for candidate prioritisation. QSAR and AI/ML-assisted approaches are further considered as complementary tools for expanding chemical-space exploration, predicting biological activity and ADMET properties, and enabling multi-parameter candidate prioritisation. Importantly, computational affinity scores are not treated as sufficient evidence of target inhibition or therapeutic efficacy. Instead, the review emphasises a progressive validation framework in which computationally prioritised candidates are evaluated through biochemical target-engagement studies, cellular assays, and, where appropriate, in vivo models. Experimental observations can subsequently inform structure–activity relationship analysis, lead optimisation, and iterative computational rescreening, establishing a closed-loop strategy for integrating molecular recognition, dynamic behaviour, binding energetics, pharmacokinetic properties, toxicity, and biological activity during cannabinoid lead development. The review critically examines key limitations of current computational approaches, including incomplete treatment of ligand flexibility, scoring-function limitations, protein conformational heterogeneity, limited experimental validation, dataset bias in AI/ML models, and translational challenges associated with cannabinoid solubility, bioavailability, metabolism, receptor promiscuity, psychoactive liabilities, and off-target effects. Collectively, current evidence indicates that computationally guided cannabinoid discovery is a promising but predominantly preclinical strategy. Further progress will depend on integrating CADD, molecular simulation, AI-assisted molecular design, uncertainty-aware prediction, and experimental validation into iterative multi-objective optimisation frameworks that jointly address potency, selectivity, pharmacokinetics, toxicity, and translational relevance.
ER -