Cannabis as a source of novel therapeutics: A computational study combining LBDD and structure-based docking/dynamics to identify novel drug-like compounds for colorectal cancer.

Khaddam Allah K.; Mkhayar K.; Haloui R.; Ballari A.; Daoui O.; El Khattabi K.; El Abbouchi A.; Chtita S.; El Khattabi S. · Journal, genetic engineering & biotechnology · 2026

**Background & Methods** This computational study employed a comprehensive drug discovery workflow combining 3D-QSAR modeling, molecular docking, and molecular dynamics simulations to identify novel cannabis-derived compounds targeting cyclin-dependent kinase 2 (CDK2) for colorectal cancer treatment. A dataset of 33 cannabis phytochemicals was screened using CoMFA and CoMSIA models with internal/external validation, followed by drug-likeness assessment and ADMET profiling to identify lead candidates against HCT-116 colorectal cancer cells. **Key Findings** • The developed CoMFA and CoMSIA 3D-QSAR models demonstrated robust predictive performance (CoMFA: Q2=0.651, R2=0.986; CoMSIA: Q2=0.665, R2=0.985), generating 20 novel molecules (V1-V20) with enhanced antiproliferative activity exceeding the most active original compounds. • Four compounds (V2, V5, V7, V8) emerged as lead candidates based on drug-likeness criteria and favorable ADMET properties, with V7 and V8 demonstrating superior CDK2 binding affinity compared to the reference compound and high structural stability within the active site. • Molecular dynamics simulations (100 ns) confirmed stability of V7 and V8 protein-ligand complexes, with converged RMSD profiles, low residue fluctuations, and maintained interactions throughout simulation; V8 exhibited the most favorable dynamic behavior with optimal radius of gyration, solvent-accessible surface area, and molecular surface area descriptors. **Dosage & Administration** Not reported. **Safety & Adverse Effects** Not reported. **Evidence Quality** This study represents early-stage computational drug discovery with significant limitations. As a purely in silico investigation, findings lack experimental validation through cell culture assays, animal models, or clinical trials. The small dataset (33 compounds) and reliance on computational predictions without biochemical confirmation of CDK2 inhibition substantially limit translational relevance. The study provides theoretical framework for identifying promising candidates but cannot establish efficacy or safety profiles. Further research requires synthesis of identified compounds (V7, V8) followed by in vitro enzyme kinetics, cell proliferation assays, and toxicology studies before advancing toward preclinical development. This represents hypothesis-generating research rather than evidence-based therapeutic advancement.

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