In December, The Conversation hosted a webinar on AI’s revolutionary role in drug discovery and development.

Science and technology editor Eric Smalley interviewed Jeffrey Skolnick, eminent scholar in computational systems biology at Georgia Institute of Technology, and Benjamin P. Brown, assistant professor of pharmacology at Vanderbilt University.

Skolnick has developed AI-based approaches to predict protein structure and function that may help with drug discovery and finding off-label uses of existing drugs. Brown’s lab works on creating new computer models that make drug discovery faster and more reliable. Below is a condensed and edited version of the interview.

Let’s start with the big picture. How is AI changing biomedical research and drug discovery, and what is the potential we are talking about?

Skolnick: The upside, potentially, is very large. One of the frustrating things about drug discovery is that, in spite of the fact that the people doing it are extraordinarily intelligent and have done an extraordinarily good job, the success rate is very low. About 1 in 5 drugs will have negative health effects that outweigh its benefits. Of the ones that pass, roughly half don’t work.

In drug development, there are several key issues: Can you predict which target is driving a particular disease? Once this target is identified, how can you guarantee the drug is going to work and isn’t simultaneously going to kill you?

These are outstanding problems in drug discovery in which AI can play an important, though not 100% guaranteed, role. Unlike us, AI can look at basically all available knowledge. On a good day it makes strong and true connections called “insights,” and on a bad day it does what is called “hallucinating” and sees things that are weak and probably false.

At the end of the day, many........

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