Scientists use machine learning to fast-track drug formulation development

Scientists at the University of Toronto have successfully tested the use of machine learning models to guide the design of long-acting injectable drug formulations. The potential for machine learning algorithms to accelerate drug formulation could reduce the time and cost associated with drug development, making promising new medicines available faster. The study was published today in Nature Communications and is one of the first to apply machine learning techniques to the design of polymeric long-acting injectable drug formulations. The multidisciplinary research is led by Christine Allen from the University of Toronto's department of pharmaceutical sciences and Alán Aspuru-Guzik, from the departments of chemistry and computer science. Both researchers are also members of the Acceleration Consortium, a global initiative that uses artificial intelligence and automation to accelerate the discovery of materials and molecules needed for a sustainable future.