HER2 targeted structure prediction and analysis based on artificial intelligence

Abstract

HER2 is a crucial marker in cancer diagnosis and targeted treatment. Accurate structure prediction and analysis of HER2 are vital for understanding its function and designing effective therapies. Our study proposes an end-to-end and artificial intelligence approach that uses deep learning frameworks to predict and analyze HER2s structure. Using top-notch machine learning algorithms, we trained a model on a comprehensive dataset of HER2 sequences and structures. The model showed impressive accuracy in forecasting HER2s tertiary structure, helping identify potential functional areas and critical interaction points. Moreover, our analysis provided new insights into HER2s structural changes and stability, revealing potential regulation mechanisms for targeted therapies. We used advanced bioinformatics tools to validate our predictions and ensure their reliability. This research marks a significant step in understanding HER2s molecular structure and lays a solid groundwork for personalized cancer treatments. By harnessing artificial intelligence, our study offers a promising path for precise medicine and targeted treatments for HER2-overexpressing cancers.