Accelerating how new drugs are made with machine learning

Researchers have developed a platform that combines automated experiments with AI to predict how chemicals will react with one another, which could accelerate the design process for new drugs. A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster. Emma King-Smith Predicting how molecules will react is vital for the discovery and manufacture of new pharmaceuticals, but historically this has been a trial-and-error process, and the reactions often fail. To predict how molecules will react, chemists usually simulate electrons and atoms in simplified models, a process that is computationally expensive and often inaccurate. Now, researchers from the University of Cambridge have developed a data-driven approach, inspired by genomics, where automated experiments are combined with machine learning to understand chemical reactivity, greatly speeding up the process. They've called their approach, which was validated on a dataset of more than 39,000 pharmaceutically relevant reactions, the chemical 'reactome'. Their results , reported in the journal Nature Chemistry , are the product of a collaboration between Cambridge and Pfizer.