Abstract: Glutathione (GSH) plays a crucial role in regulating intracellular reactive oxygen species (ROS) levels. Depletion of glutathione in cancer cells leads to elevated ROS levels, subsequently inducing cellular inactivation and apoptosis, thereby establishing GSH as a promising therapeutic target for cancer treatment. Capitalizing on the propensity of α,β-unsaturated carbonyl compounds to undergo Michael addition reactions with glutathione, we developed an innovative AI-driven virtual screening protocol using ChemDraw, Chem3D, Gaussian, and Chemprop software. This approach involved constructing a molecular database of anticancer compounds and computationally characterizing their reactivity with glutathione. The AI prediction results demonstrated remarkable consistency with experimentally reported data in the literature, validating the scientific rigor and reliability of our virtual screening methodology. Potential drug candidates identified through AI screening were subsequently evaluated via chemical kinetics and pharmacological assays, yielding lead compounds worthy of further investigation. This AI-enhanced drug discovery approach significantly reduces screening costs while improving both efficiency and accuracy. The experimental framework serves as an effective pedagogical tool for chemical biology or chemoinformatics courses, where AI technology facilitates deeper understanding of core concepts. This innovative teaching method enhances curriculum sophistication, stimulates student engagement, and cultivates critical thinking and innovative capacity, ultimately fostering the development of exceptional talents for future scientific challenges.

Key words: Glutathione, Anticancer drug, Artificial intelligence, Virtual screening, Michael addition reaction