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CAMKK2 inhibitors have therapeutic effects on cancer, metabolic disorders, diabetes and osteoporosis. However, high-quality CAMKK2 inhibitors are still lacking. Natural product databases were screened for potential inhibitors using bioinformatics strategies such as pharmacophore models, molecular docking, molecular dynamics simulation. Two hit compounds, STOCK1N-53910 and STOCK1N-66485, were got via Lipinski rule, Veber rule, PAINS rule, pharmacophore models and molecular docking. The results of molecular docking showed hit compounds had similar binding patterns to the active control. Hit compounds had low toxicity by prediction of ADME/T. Molecular dynamics simulation and energy decomposition showed the binding energy of hit compounds was better than active control, and had higher affinity. These results suggest STOCK1N-53910 and STOCK1N-66485 may inhibit CAMKK2 and could be as candidate drugs for further research.

Identification of potential CAMKK2 inhibitors based on virtual screening and molecular dynamics simulation