Connor Coley

Connor Coley, 29, developed open-source software that uses artificial intelligence to help discover and synthesize new molecules. The suite of tools, called ASKCOS, is used in production by over a dozen pharmaceutical companies, and tens of thousands of chemists, to create new medicines, new materials, and more efficient industrial processes.

One of the largest bottlenecks in developing new molecules has long been identifying interesting candidates to test. This process has played out in more or less the same way for decades: make a small change to a known molecule, and then test the novel creation for its biological, chemical, or physical properties.

Coley’s approach includes a form of generative AI for chemistry. A chemist flags which properties are of interest, and AI-driven algorithms suggest new molecules with the greatest potential to have those properties. The system does this by analyzing known molecules and their current properties, and then predicting how small structural changes are likely to result in new behaviors.

As a result, chemists should spend less time testing candidates that never pan out. “The types of methods that we work on have led to factors of maybe two, three, maybe 10 [times] reduction in the number of different shots on goal you need to find something that works well,” says Coley, who is now an assistant professor of chemical engineering and computer science at MIT. 

Once it identifies the candidate molecules, Coley’s software comes up with the best way to produce them. Even if chemists “imagine or dream up a molecule,” he says, figuring out how to synthesize something isn’t trivial: “We still have to make it.”

To that end, the system gives chemists a “recipe” of steps to follow that are likely to result in the highest yields. Coley’s future work includes figuring out how to add laboratory robots to the mix, so that even more automated systems will be able test and refine the proposed recipes by actually following them.