Network Based Identification of Holistic Drug Target for Parkinson Disease and Deep Learning assisted Drug Repurposing.

Abstract

Parkinson is a neurodegenerative disorder of the nervous system involved with disrupting the motor activity of the body. The current pathogenesis of the disorder is incomplete resulting in widespread use of exogenous medical treatments targeting the dopamine quantity, posing a major challenge in appropriate drug development. The plethora of high throughput techniques in the last decade has yielded a vast amount of Omics dataset with an opportunity of providing a holistic overview of the disease workings and dynamics. We integrated the Parkinson disease Omics datasets using network-based integration strategies to build Parkinson disease network. The most impactful and resilient node of the network was selected as a drug target. Deep learning based virtual screening estimator was built from physicochemical properties of different compounds having variable affinity to target binding. Virtual screening of FDA approved drugs repurposed 19 drugs with 25% of them falling under insomnia treatment; the most prevalent sleep disorder in Parkinson patients. Source Code of the project is available at https://github.com/aysanraza/pd_repurposing_protocol