Researchers have used artificial intelligence techniques to massively accelerate the search for Parkinson's disease treatments. The researchers designed and used an AI-based strategy to identify compounds that block the clumping, or aggregation, of alpha-synuclein, the protein that characterises Parkinson's.
The team used machine learning techniques to quickly screen a chemical library containing millions of entries, and identified five highly potent compounds for further investigation. Proteins are responsible for important cell processes, but when people have Parkinson's, these proteins go rogue and cause the death of nerve cells. When proteins misfold, they can form abnormal clusters called Lewy bodies, which build up within brain cells stopping them from functioning properly.
This has been a major obstacle in Parkinson's research, because of the lack of methods to identify the correct molecular targets and engage with them. This technological gap has severely hampered the development of effective treatments. "Instead of screening experimentally, we screen computationally," said Vendruscolo, who is co-Director of the Centre for Misfolding Diseases."By using the knowledge we gained from the initial screening with our machine learning model, we were able to train the model to identify the specific regions on these small molecules responsible for binding, then we can re-screen and find more potent molecules.
The research was conducted in the Chemistry of Health Laboratory in Cambridge, which was established with the support of the UK Research Partnership Investment Fund to promote the translation of academic research into clinical programmes.Robert I. Horne, Ewa A. Andrzejewska, Parvez Alam, Z. Faidon Brotzakis, Ankit Srivastava, Alice Aubert, Magdalena Nowinska, Rebecca C. Gregory, Roxine Staats, Andrea Possenti, Sean Chia, Pietro Sormanni, Bernardino Ghetti, Byron Caughey, Tuomas P. J.
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