Using AI to accelerate drug development
Drug development is an arduous process that costs billions of dollars and can last for years or even decades. Whether scientists are trying to understand the potential interactions of two drugs or develop new applications for an existing medication, pharmaceutical research features frequent wrong turns and dead ends.
“Often, when we use machine learning to train neural networks, we’re starting from scratch,” says Bing Hu, a PhD student at the Cheriton School of Computer Science. “But by drawing on the enormous amount of domain specific knowledge coming from biology and medicine, we’re able to build more efficient, more accurate models whose predictions consistently match-up with existing data from the real world.”