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.

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.

Using Artificial intelligence (AI) or machine learning technology, a team of researchers has predicted outcomes in judicial decisions at the European Court of Human Rights (EctHR) with 79 per cent accuracy. The AI method, developed by researchers from University College London (UCL), University of Sheffield and US-based University of Pennsylvania is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm. "We don't see AI replacing judges or lawyers but we think they will find it useful for rapidly identifying patterns in cases that lead to certain outcomes," said Nikolaos Aletras, who led the study at UCL's computer science department. "It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights," Aletras added. In developing the method, the team found that judgements by the ECtHR are highly correlated to non-legal facts rather than directly legal arguments, suggesting that judges of the Court are'realists' rather than'formalists'.