AI-based Identification of Stroke Targets and Research on Related Tibetan Medicines

In recent years, stroke has become one of the leading causes of death and disability worldwide, yet the specific molecular mechanisms remain not fully elucidated. This study aims to explore the complete structure of IL13 obtained through AI to design corresponding drugs for the treatment of stroke. We used AlphaFold to predict the Alpha-helix in the structure of the IL13 protein, and the pLDDT scores of IL13 from AlphaFold's predictions were all above 75, indicating a high degree of credibility for the predicted alpha-helix structure, although further validation may be required. These results demonstrate that the significance of AlphaFold in the field of drug discovery cannot be underestimated, as it not only accelerates the resolution of protein structures but also promotes the entire process from target identification to drug design. It may provide new targets for the treatment of stroke. At the same time, AI has tremendous potential in predicting the mechanisms of action and ingredient structures of Tibetan medicines, especially in aspects such as compound structure prediction, target identification, network pharmacology analysis, and drug metabolism prediction. With the accumulation of data and technological advancements, AI will also play an increasingly important role in the research and development of Tibetan medicines.[1]