Pharmaceutical companies are using artificial intelligence to streamline the process of discovering new medicines. Machine-learning models can propose new molecules that have specific properties which could fight certain diseases, doing in minutes what might take humans months to achieve manually. But there’s a major hurdle that holds these systems back: The models often suggest new molecular structures that are difficult or impossible to produce in a laboratory. If a chemist can’t actually make the molecule, its disease-fighting properties can’t be tested. A new approach from MIT researchers constrains a machine-learning…
