Episode 16: Building AI for Life Sciences

Hello, I'm Andrew Main, and this is the OpenAI Podcast. On today's episode, we're talking with Research Lead Joy Jiao, and Product Lead Yunyun Wang about OpenAI for Life Sciences. We'll explore what new models are making possible in biology and medicine, and what it takes to deploy the most advanced capabilities responsibly.

This allows it to kind of reach new levels of difficulty and discovery that we didn't think was even possible before.

Putting like really capable expert level knowledge in the hands of a greater amount of people.

One of the taglines was to scale test time compute to cure all disease.

Yes.

So that is like [chuckles] our team tagline.

We started off with just a basic API, and then we had ChatGPT, which is more conversational, was really good for text, as code became a capability, went through basically code models and then Codex. Now that you're getting more scientists in the life sciences working on these systems, does that mean things have to evolve to help with the way researchers might work with these tools?

Yeah. We're really excited to, uh, build and deploy the Life Sciences model series. So this is a new biochemistry focused model series that's really anchored on these very complex life science research workflows, and we're focused on adding, um, new like mechanistic understanding, uh, starting with genomics understanding and protein understanding, and really focused on early discovery use cases because we feel like that's like one of the core bottlenecks that, uh, greater thinking time, greater compute, and really like leveraging like more capable AI models can help, um, meaningfully scale some of these like research barriers. And I think there's also like a, a model orchestration piece of actually how to embed this into workflows, and it's been really great, uh, first off, w- having all these different product surfaces to deploy to. We're seeing a lot of really great like literature synthesis workflows happening on, uh, ChatGPT, and, uh, these models really push the frontier of like long trajectory agentic workflows, and we're really able to empower that on, uh, Codex. And more on the model orchestration piece is that I think for enterprise use cases, there's like this reproducibility and repeatability element, and we are trying to overcome this by working on like some of the life sciences research plugins that we're shipping for very specific translational, uh, bio users. So the life sciences research plugin has over 50 skills, which are essentially templatized repeatable workflows that, um, if you need to whether do some sort of cross evidence match and search across various different papers or do, uh, pathway analysis, something that's like, uh, repeatable that you often do, you can have like almost like a one click deploy option by using our life sciences plugins on top, and that's also how we're kind of seeing, uh, the balance between, uh, scaling for very specialized, uh, purposes, uh, something we're hoping to get into is maybe clinical purposes, but also make it still very general use for all foundational biology.