Abstract
Oxidative stress, characterized by the damaging accumulation of free radicals, is associated with various diseases, including cardiovascular, neurodegenerative, and metabolic disorders. The transcription factor Nrf2 is pivotal in cellular defense against oxidative stress by regulating genes that detoxify free radicals, thus maintaining redox homeostasis and preventing cellular aging. Keap1 plays a regulatory role through its interaction with Nrf2, ensuring Nrf2 degradation under homeostatic conditions and facilitating its stabilization and nuclear translocation during oxidative stress. In the initial stage of our study, we conducted in vitro assays on HaCaT cells, a human keratinocyte cell line, to measure the expression levels of Nrf2 to reveal the activity of promising medicinal plants, which were then selected for further evaluation. Subsequently, this study leverages in silico techniques, integrating machine learning with molecular docking and dynamics, to screen natural compounds that potentially activate Nrf2. Data from the ChEMBL database were categorized into active and inactive compounds and used for training different machine-learning models to predict potential Nrf2 activators. The best-performing model was used to select compounds for further evaluation via molecular docking and dynamics, assessing their interactions with Keap1/Nrf2. The LC-MS/MS-based chemical profiles also validated the presence of these chemical compounds. This approach underscores the synergy between in vitro bioassays and in silico approaches in identifying Nrf2 activators, offering a cost-effective strategy for drug development.