“The use of AI to develop machine learning approaches to drug discovery for protein aggregation diseases like Parkinson’s, has definitely arrived," said Michael S. Okun, M.D., National Medical Advisor for the Parkinson’s Foundation and Director of the Fixel Institute for Neurological Diseases at the University of Florida. "The over 20-fold improvement over typical high-throughput drug screening hit rates was impressive in this study and will add to the list of potential drugs to consider for clinical trials," added Okun, who was not involved in the research.

Nearly 90,000 Americans are diagnosed with Parkinson's disease annually, according to the Parkinson's Disease Foundation, with a million people in the U.S. currently living with the disease. Despite this, there are currently no curative treatments for the disease, only drugs to manage symptoms which include tremors, balance and mobility issues and muscle stiffness.

"Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidates," said Vendruscolo. “For us, this means we can start work on multiple drug discovery programmes – instead of just one. So much is possible due to the massive reduction in both time and cost – it’s an exciting time."

However, discovering promising new compounds is only one, very early step in actually getting tried and tested drugs to patients.

“Whether this innovation however, will speed discovery of new Parkinson’s therapeutics is complicated, as introducing more compounds could actually slow the pipeline," said Okun. "Thus, a parallel and large investment will be needed in basic science research to better understand the pathogenesis of Parkinson’s disease and to more precisely apply this, and other novel AI derived drug discovery methodologies.”