Exploring the Potential of Deep Learning in Parkinson's Disease Drug Discovery

Complex and complex neurological illnesses like Parkinson's Disease (PD) are difficult to investigate. This publication introduces “Neural Pathway Profiling for Parkinson's Disease Drug Discovery.” It leverages cutting-edge deep learning. GCNs explain molecular interactions, LSTMs examine temporal sequences, and VAEs learn by forming new patterns. Our research uses genes, proteins, and clinical data to explain how Parkinson's disease's various components interact in complex ways. LSTMs illustrate how clinical data is connected to time, GCNs reveal how molecules interact in biological networks, and VAEs discover hidden chemical structures for generative learning. The blended design reveals key brain connections and Parkinson's disease therapeutic targets.