Machine learning tool simplifies one of the most widely used reactions in the pharmaceutical industry

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In the past two decades, the carbon-nitrogen bond forming reaction, known as the Buchwald-Hartwig reaction, has become one of the most widely used tools in organic synthesis, particularly in the pharmaceutical industry given the prevalence of nitrogen in natural products and pharmaceuticals.

This powerful reaction has revolutionized the way nitrogen-containing compounds are made in academic and industrial laboratories, but it requires lengthy, time-consuming experimentation to determine the best conditions for a highly effective reaction.

User guides and cheat sheets have evolved in the nearly 30 years since this reaction was discovered, and they can provide some direction, Rinehart explained, but experimentation is often necessary. Basically, a trial-and-error process in a lab.recognized was ripe for intervention by informatics methods," Denmark said.

"One of Ian's biggest contributions was figuring out the workflow to decide what experiments to do to get a valid predictive model with about 3,500 experiments and still be able to make predictions without an enormous database," Denmark said."We tested them and found with pretty good statistics that the conditions were producing compounds when we expected," Denmark said.

"So, we have now run or talked about so many of these couplings that we have a good intuition about what's going to happen, but someone who hadn't run hundreds or thousands of these might not have a good first guess. We have taught a model at a much more granular level [than user guides] to have an intuition. It's not perfect. But that's kind of the point. It doesn't have to be. It just has to get you to the answer faster," Rinehart said.

 

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