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AI in Action: Neural networks learn the art of chemical synthesis

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Science  07 Jul 2017:
Vol. 357, Issue 6346, pp. 27
DOI: 10.1126/science.357.6346.27

When fed examples of successful chemical reactions, new software learns how to build molecules on its own.

Organic chemists are experts at working backward. Like master chefs who start with a vision of the finished dish and then work out how to make it, many chemists start with the final structure of a molecule they want to make, and then think about how to assemble it. “You need the right ingredients and a recipe for how to combine them,” says Marwin Segler, a graduate student at the University of Münster in Germany. He and others are now bringing artificial intelligence (AI) into their molecular kitchens.

They hope AI can help them cope with the key challenge of molecule-making: choosing from among hundreds of potential building blocks and thousands of chemical rules for linking them. For decades, some chemists have painstakingly programmed computers with known reactions, hoping to create a system that could quickly calculate the most facile molecular recipes. However, Segler says, chemistry “can be very subtle. It's hard to write down all the rules in a binary way.”

So Segler, along with computer scientist Mike Preuss at Münster and Segler's adviser Mark Waller, turned to AI. Instead of programming in hard and fast rules for chemical reactions, they designed a deep neural network program that learns on its own how reactions proceed, from millions of examples. “The more data you feed it the better it gets,” Segler says. Over time the network learned to predict the best reaction for a desired step in a synthesis. Eventually it came up with its own recipes for making molecules from scratch.

The trio tested the program on 40 different molecular targets, comparing it with a conventional molecular design program. Whereas the conventional program came up with a solution for synthesizing target molecules 22.5% of the time in a 2-hour computing window, the AI figured it out 95% of the time, they reported at a meeting this year. Segler, who will soon move to London to work at a pharmaceutical company, hopes to use the approach to improve the production of medicines.

Paul Wender, an organic chemist at Stanford University in Palo Alto, California, says it's too soon to know how well Segler's approach will work. But Wender, who is also applying AI to synthesis, thinks it “could have a profound impact,” not just in building known molecules but in finding ways to make new ones. Segler adds that AI won't replace organic chemists soon, because they can do far more than just predict how reactions will proceed. Like a GPS navigation system for chemistry, AI may be good for finding a route, but it can't design and carry out a full synthesis—by itself.

Of course, AI developers have their eyes trained on those other tasks as well.

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