  Parkinson's disease is a progressive and slowly developing neurodegenerative
disease, characterized by dopaminergic neuron loss in the substantia nigra
region of the brain. Despite extensive research by scientists, there is not yet
a cure to this problem and the available therapies mainly help to reduce some
of the Parkinson's symptoms. Drug repurposing (that is, the process of finding
new uses for existing drugs) receives more appraisals as an efficient way that
allows for reducing the time, resources, and risks associated with the
development of new drugs. In this research, we design a novel computational
platform that integrates gene expression data, biological networks, and the
PDOD database to identify possible drug-repositioning agents for PD therapy. By
using machine learning approaches like the RWR algorithm and PDOD scoring
system we arrange drug-disease conversions and sort our potential sandboxes
according to their possible efficacy. We propose gene expression analysis,
network prioritization, and drug target data analysis to arrive at a
comprehensive evaluation of drug repurposing chances. Our study results
highlight such therapies as promising drug candidates to conduct further
research on PD treatment. We also provide the rationale for promising drug
repurposing ideas by using various sources of data and computational
approaches.
