By thinking of atoms as letters and molecules as words, artificial intelligence software from IBM is now employing the same methods computers use to translate languages to predict outcomes of organic chemical reactions, which could speed the development of new drugs.
In the past 50 years, scientists have tried to teach computers how chemistry works so that computers can help predict the results of organic chemical reactions. However, organic chemicals can be extraordinarily complex, and simulations of their behavior can prove time-consuming and inaccurate.
Instead, researchers at IBM took the kind of AI program normally used to translate languages and applied it toward organic chemistry. "Instead of translating English into German or Chinese, we had the same artificial intelligence technology look at hundreds of thousands or millions of chemical reactions and had it learn the basic structure of the 'language' of organic chemistry, and then had it try to predict the outcomes of possible organic chemical reactions," says study co-author Teodoro Laino at IBM Research in Zurich.
"We want to help chemists design new synthesis routes for organic compounds," Laino says. Synthesizing pharmaceuticals and other complex organic compounds is often a difficult task, "maybe requiring 30 or 40 steps," he explains. "There's a huge effort in the commercial sector to find shortcuts to skip a couple of steps, with the benefit of decreasing time and increasing yields."
The new AI program is an artificial neural network, in which components dubbed neurons are fed data and cooperate to solve a problem, such as translating a sentence. The neural network then repeatedly adjusts the connections between its neurons and sees if these new patterns of connections is better at solving the problem. Over time, the neural net discovers which patterns are best at computing solutions, mimicking the process of learning in the human brain.
