Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the accumulation of misfolded proteins and impaired protein degradation mechanisms. Dysregulation of protein degradation processes, including autophagy and the ubiquitin-proteasome system, has been implicated in the pathogenesis of PD. Recently, artificial intelligence (AI) has emerged as a powerful tool to enhance our understanding of protein degradation in PD. This abstract provides an overview of the advancements in studying protein degradation in PD with the aid of AI. The integration of AI techniques, such as machine learning and data mining, has enabled the identification and characterization of protein degradation pathways involved in PD. By analyzing large-scale protein-protein interaction networks, AI algorithms have revealed key interactions and pathways underlying protein degradation dysfunction in PD. Furthermore, AI models can predict the efficiency of protein degradation processes and identify potential targets for enhancing protein degradation in PD, aiding in the development of novel therapeutic interventions.AI-based approaches have also been instrumental in drug discovery and target identification, as they can screen vast databases of compounds to identify potential drugs or small molecules that modulate protein degradation pathways relevant to PD. Additionally, deep learning algorithms have facilitated the analysis of protein structures, predicting protein stability and folding patterns that impact protein degradation. Moreover, AI has played a crucial role in the identification of protein biomarkers associated with protein degradation dysfunction in PD. These biomarkers can aid in early diagnosis and monitoring of the disease, enabling timely intervention and personalized treatment strategies. The advancements presented in this abstract highlight the transformative potential of AI in elucidating the intricate mechanisms of protein degradation in PD. Collaborations between AI researchers, biologists, and clinicians are essential to translate these findings into effective diagnostic tools and therapeutic interventions for PD patients.

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