HomeIndustriesScientists are accelerating the seek for Parkinson's treatments using AI

Scientists are accelerating the seek for Parkinson's treatments using AI

Researchers on the University of Cambridge have used AI to dramatically speed up the search for brand new treatments for Parkinson's disease.

By using machine learning techniques, they were capable of screen tens of millions of potential drug compounds and discover essentially the most promising candidates 10 times faster and 1,000 times cheaper than traditional methods.

Parkinson's disease is a fancy, progressive neurodegenerative disease that affects roughly 6 million people worldwide. By 2040, this number is predicted to triple.

There is currently no treatment that may reliably slow or stop the progression of the disease.

The traditional means of searching vast chemical libraries to seek out potential drug candidates is incredibly slow, expensive and infrequently unsuccessful.

“One path to finding potential treatments for Parkinson's requires identifying small molecules that may inhibit the aggregation of alpha-synuclein, a protein closely linked to the disease,” said lead researcher Professor Michele Vendruscolo said the University of Cambridge.

“But that is an especially time-consuming process – just identifying a number one candidate for further testing can take months and even years.”

To address this challenge, Vendruscolo and his team developed a five-step machine learning approach. The study was published in .

  1. Start with a small set of compounds identified through simulations that show the potential to dam the clumping of the alpha-synuclein protein, which is the foundation reason for Parkinson's. Then test their effectiveness experimentally.
  2. Use the outcomes to coach a machine learning model to predict which molecular structures and properties make a compound effective in stopping protein aggregation.
  3. Use the trained model to quickly search a virtual library of tens of millions of compounds and predict essentially the most effective candidates.
  4. Experimentally validate the AI-selected top candidates within the lab. Feed these results back into the model to further refine its predictive capabilities.
  5. Repeat this cycle of computational prediction and experimental testing, with the AI ​​model getting smarter with each round and specializing in the best-performing compounds.
The University of Cambridge Iterative Parkinson's Drug Discovery System. Source: Natural chemical biology (open access)

Over several iterations, the optimization rate – the proportion of compounds tested that inhibited α-synuclein clumping linked to Parkinson's – increased from 4% to over 20%.

Additionally, the compounds the AI ​​found were, on average, much more potent than any previously identified. Some showed promising activity at eight-fold lower doses. They were also more chemically diverse, because the model discovered potent compounds that differed from known structures.

“Machine learning is having an actual impact on drug discovery – it hurries up your entire means of identifying essentially the most promising candidates,” said Vendruscolo.

“By leveraging the insights we gained from the initial screening with our machine learning model, we were capable of train the model to discover the precise regions on these small molecules which might be liable for binding, after which we are able to screen again perform and find more practical molecules.”

“For us, this implies we are able to start working on multiple drug discovery programs quite than simply one. So much is feasible because of the large time and value reduction – it’s an exciting time.”

The researchers emphasize that that is only the start of what AI-first approaches could enable in drug development for Parkinson's and other diseases characterised by protein misfolding and aggregation.

With further development and bigger training data sets, the predictive power of those models should only improve.

While there continues to be a protracted option to go to rework these AI-identified candidates into approved treatments, this study shows how machine learning, cleverly combined with experimental biology, can significantly speed up the early stages of drug development.

This builds on a body of research addressing the challenge of finding latest, novel drug treatments including from MIT and Tuftswhich recently built a model that may sift through about 100 million compounds per day.

Several Antibiotic discovery models have made experimental compounds, a few of that are on the option to clinical trials.

Another Major project Last 12 months, in collaboration with Moorfields Eye Hospital within the UK, eye scans were carried out to detect the early signs of Parkinson's – a novel method made possible by AI.

With this latest study aimed toward discovering effective Parkinson's treatments, AI methods show great potential for redefining medicine and healthcare.


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