Mizzou research team advances search for Parkinson’s Disease treatment with AI-powered software

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Aug. 29, 2024

A research team led by University of Missouri professor Dmitri Kireev is a forerunner in the challenge to find an effective treatment for Parkinson's disease.

Kireev and several postdoctoral students competed in the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge hosted by the Structural Genomics Consortium in Toronto. Utilizing FRASE-bot, an artificial intelligence (AI) powered software developed by Kireev's team, they discovered molecular hits for the most common mutated gene associated with Parkinson's disease.

To find an effective treatment for Parkinson's, researchers must first breach what causes the neurogenerative disease - the mutated protein LRRK2. Blocking the protein's activity with an effective chemical compound, or a hit, can lead to potential treatment. Kireev and his team used FRASE-bot to discover an effective blocker, molecules that might become a potential treatment.

"This is an exciting time in Parkinson's disease research," said Kireev, who joined the Department of Chemistry in 2022. "What we discovered could be a vital step toward improving the quality of life for those impacted by this disease."

Kireev is working with a Parkinson's biologist at the University of Lille in France to continue research on the molecular compound, with the long-term goal of producing a Food and Drug Administration-approved therapy.

Kireev's research group included three postdoctoral students: two from Mizzou, Xiaowen Wang and Akhila Mettu, and one from the University of North Carolina at Chapel Hill (UNC), where Kireev was a professor at the Eshelman School of Pharmacy and the director of Computational Biophysics and Molecular Design before joining Mizzou. His future aspirations include developing a center dedicated to AI-enabled drug discovery.

"Artificial intelligence is rapidly transforming the field of drug discovery, enabling researchers to extract knowledge from massive amounts of data and use it to identify potential drug candidates," Kireev said. "AI makes the drug discovery process faster, reduces costs and increases the likelihood of success in developing effective personalized therapeutics."

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