A new platform designed by researchers from the University of Cambridge is using machine learning to rapidly speed up the process of making drugs that can effectively target disease. Called the ‘reactome’, it combines big data and genomics to discover how different chemicals react with each other.
Dr Alpha Lee, who led the research – published today, says: “The dataset we trained the model on is massive – it will help bring the process of chemical discovery from trial-and-error to the age of big data.”
Chemists can use the precision tool to tweak molecules as they go, transforming a process that would previously begin again from scratch when issues arose.
The reactome will uncover “hidden relationships between components” Dr Lee says, paving the way for a fast pipeline of new pharmaceutical drugs.
“A deeper understanding of the chemistry could enable us to make pharmaceuticals and so many other useful products much faster,” adds Dr Emma King-Smith from Cambridge’s Cavendish Laboratory.
The results of the research were reported today in the journal Nature Chemistry.
