Artificial intelligence (AI) in drug discovery is where computer scientists meet chemists. Machine learning algorithms filter large libraries of chemical compounds down to a few potential candidates worth testing in the lab. The computer scientist runs the first leg of the track meet before handing off the baton to the chemist for further investigation of identified hit compounds. This process, while not perfect, may reduce the timeline from drug discovery to drug approval by 4-5 years. If time is money, this approach will save biotech and pharmaceutical companies millions, if not billions of dollars. The goal for athletics is: bigger, stronger, faster. The goal for drug development is: cheaper, faster, safer. In this episode we highlight some of the pros and cons for using AI to meet the challenging demands of bringing therapeutics to market. The current FDA is well prepared to handle new drug applications, but how about regulating algorithms? In our changing world I find myself asking which is more valuable, the drug or the algorithm? 

You're in for an exciting episode. We hope you enjoy!