AI Combined with Molecule-Making Machine Could Make Complex Chemistry Automated and Accessible

A team of researchers had earlier pioneered the development of simple chemical building blocks for small molecules. They had also developed an automated molecule-making machine that snaps together the buildings blocks to create a wide range of possible structures. Now, the team has combined artificial intelligence (AI), “building-block” chemistry and a molecule-making machine to find the best general reaction conditions for synthesizing chemicals important to biomedical and materials research – a finding that could speed innovation and drug discovery as well as make complex chemistry automated and accessible.

With the machine-generated optimized conditions, researchers at the University of Illinois at Urbana-Champaign (Champaign, IL, USA), the Polish Academy of Sciences’ Institute for Organic Chemistry (IOC PAS, Warsaw, Poland), and the University of Toronto (Toronto, ON, Canada) doubled the average yield of a special, hard-to-optimize 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. An automated approach with generalized conditions could help standardize how chemists make some products, addressing the problem of reproducibility.

Published studies reflect conditions that are popular or convenient, rather than the best, so a systematic approach that included diverse data and negative results was necessary, according to the researchers. First, the team ran the entire matrix of possible combinations using the building-block chemistry through an algorithm to group together similar reactions. Then, the AI sent instructions, inputted to a machine in the Molecule Maker Lab located in the Beckman Institute for Advanced Science and Technology, to produce representative reactions from each cluster. The information from those reactions fed back into the model; the AI learned from the data and ordered more experiments from the molecule machine.

The process identified conditions that doubled the average yield of a challenging class of reactions, called heteroaryl Suzuki-Miyaura coupling, crucial for many biological and materials-relevant compounds. The machine-learning process could also be applied to other broad areas of chemistry to find the best reaction conditions for other types of small molecules or even larger organic polymers, the researchers say.

“Generality is critical for automation, and thus making molecular innovation accessible even to nonchemists,” said study co-leader Dr. Martin D. Burke. “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.”

Related Links:
University of Illinois at Urbana-Champaign 
IOC PAS 
University of Toronto