AI-Driven Discovery and Delivery Systems for Anti-Parkinsonian Agents

Parkinson's disease is the second most widely diagnosed neurodegenerative disorder in the world, affecting more than 10 million people, with current drug therapies for treatment providing limited symptomatic relief and no effect on stopping disease progression. The integration of AI and Pharmaceutical Sciences has led to a change in the approach to discovering and developing new anti-Parkinsonian drugs. This review presents a comprehensive overview of the many applications of Machine Learning (ML), Deep Learning (DL), Graph Neural Networks (GNNs) and Generative AI to the PD drug development process.We have critically examined AI-guided target identification, including disruption of α-synuclein (SNCA) fibrils, inhibition of Leucine Rich Repeat Kinase 2 (LRRK2), modulation of GBA1/Glucocerebrosidase and restoration of mitochondrial functions via PINK1/Parkin along with the application of Transformer-based models such as AlphaFold2 and RoseTTAFold to elucidate the structures of Pathological proteins and create the basis for conducting AI-accelerated Virtual Screening studies, yielding exciting lead compounds.Although tremendous advancements have been made, many obstacles need to be overcome, such as obtaining rare disease data, improving the interpretability of the data used in AI models, developing appropriate regulatory pathways, and bridging the gap between pre-clinical and clinical data.