New AI system could speed up drug discovery

The research team from the University of Illinois

A new system utilising artificial intelligence (AI), ‘building-block’ chemistry and a molecule-making machine could speed up innovation and drug discovery, as well as make complex chemistry automated and accessible.

Researchers at the University of Illinois Urbana-Champaign and collaborators in Poland and Canada doubled the average yield of a special, hard-to-optimise type of reaction linking carbon atoms together in pharmaceutically important molecules.

The researchers say their system provides a platform that also could be used to find general conditions for other classes of reactions and solutions for similarly complex problems.

“Generality is critical for automation, and thus making molecular innovation accessible even to non-chemists,” said study co-leader Dr Martin D Burke, an Illinois Professor of chemistry and of the Carle Illinois College of Medicine, as well as a medical doctor. 

“The challenge is the haystack of possible reaction conditions is astronomical, and the needle is hidden somewhere inside. By leveraging the power of artificial intelligence and building-block chemistry to create a feedback loop, we were able to shrink the haystack. And we found the needle.”

Automated synthesis machines for proteins and nucleic acids such as DNA have revolutionised research and chemical manufacturing in those fields, but many chemicals of importance are small molecules with complex structures, the researchers say.

Burke’s group has pioneered the development of simple chemical building blocks for small molecules. His lab also developed an automated molecule-making machine that snaps together the building blocks to create a wide range of possible structures.

However, general reaction conditions to make the automated process broadly applicable have remained elusive.

“Traditionally, chemists customise the reaction conditions for each product they are trying to make,” Burke said. “The problem is that this is a slow and very specialist-dependent process, and very hard to automate because the machine would have to be optimised every time. What we really want are conditions that work almost every time, no matter what two things you’re trying to snap together.” 

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