AI translates chemistry to predict reaction outcomes

IBM researchers have developed a program that can predict the products of organic chemistry reactions.1 Modelled on the latest language translation systems – like Google's artificial neural network – the AI picked the right product 80% of the time despite not having been taught any organic chemistry rules. 'What this tool is trying to do is imitate a top pro chemist in more or less the entire domain of organic chemistry,' says Teodoro Laino, one of the researchers involved in the study at IBM in Zurich, Switzerland. His ambitious goal is shared by other chemists who have been attempting to create a functioning AI chemist since the 1970s, when organic chemist E J Corey kick-started the field by creating a chemical knowledge database. However, making a tool based on chemistry knowledge can be time-consuming; Bartosz Grzybowski's team took 10 years to encode their Chematica retrosynthesis program with 20,000 chemical rules. Moreover, a knowledge-based AI has difficulty tackling reactions that lie outside of its rule set. 'There's a way to learn organic chemistry that's not memorising chemical rules, by just trying to find out the underlying patterns in reactions and trying to rationalise them,' Laino says, explaining the approach that his team took.