One force driving that explosion has been the Defense Advanced Research Projects Agency (DARPA). It’s wrapping up a four-year program called Make-It, of which both the Chemputer and AutoSyn are alumni. The long-term goal of the program’s manager, Anne Fischer, is to speed up the discovery of useful molecules, which has historically involved a lot of waiting around while chemists laboriously smithed atoms into novel configurations. “The slow step is always making and testing the molecules,” she says.

 But now that Make-It has helped produce robotic tools to build molecules like the Chemputer, AutoSyn, and others, she’s directing a new DARPA program, Accelerated Molecular Discovery, that looks to the next stage: developing smarter software to tell the robots what molecules to make, and how to make them.  
This is still a very new science. It’s started to explode really in the last 18 months.

 ″We’re now trying now to harness what we’ve done in Make-It and expand it out so we can teach computers how to discover new molecules,” she says.

 The secret to doing so, many believe, is machine learning. And some machines capable of rudimentary chemical learning are well underway. Connor Coley, a chemist at MIT, is a member of a team that last year paired an automated flow chemistry system with an algorithm to direct it. The algorithm trained on databases of hundreds of thousands of reactions and was able to predict recipes for new products. “It tries to understand, based on those patterns, what kind of transformations should work for new molecules it’s never seen before,” Coley said.