A5102880464- Cannabis and Cannabinoid Research
- Computational Drug Discovery Methods
- GABA and Rice Research
- Synthesis and biological activity
- Plant tissue culture and regeneration
- Cancer therapeutics and mechanisms
- CRISPR and Genetic Engineering
- Muscle metabolism and nutrition
- Polyamine Metabolism and Applications
- Analytical Chemistry and Chromatography
- Autophagy in Disease and Therapy
- Machine Learning in Materials Science
- Transgenic Plants and Applications
Jeonbuk National University
2022-2025
Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling rapid assessment compound properties before costly and time-consuming laboratory experiments. With advent technology large data availability, machine deep learning have proven predicting molecular solubility. High-precision silico solubility prediction has revolutionized development by enhancing formulation design, guiding lead optimization, pharmacokinetic parameters. These benefits result...
Abstract Cannabidiol (CBD), a nonpsychoactive compound from Cannabis , has various bioactive functions in humans and animals. Evidence suggests that CBD promotes muscle injury recovery athletes, but whether how improves endurance performance remains unclear. Here we investigated the effects of treatment on exercise mice assessed this effect involves gut microbiome. administration significantly increased treadmill running mice, accompanied by an increase oxidative myofiber composition. also...
Cannabis (Cannabis sativa L.) is widely cultivated and studied for its psychoactive medicinal properties. As the major cannabinoids are present in acidic forms plants, non-enzymatic processes, such as decarboxylation, crucial their conversion to neutral active cannabinoid forms. Herein, we detected levels of cannabidivarin (CBDV), cannabidiol (CBD), cannabichromene (CBC), Δ9-tetrahydrocannabinol (Δ9-THC) leaves vegetative shoots five commercial cultivars using a combination relatively simple...
Targeting epidermal growth factor receptor (EGFR) mutants is a promising strategy for treating non-small cell lung cancer (NSCLC). This study focused on the computational identification and characterization of potential EGFR mutant-selective inhibitors using pharmacophore design validation by deep learning, virtual screening, ADMET (Absorption, distribution, metabolism, excretion toxicity), molecular docking-dynamics simulations. A model was generated Pharmit based potent inhibitor JBJ-125,...