Open AI to help in Scientific Research and Drug Discovery

OpenAI unveiled a new model this week, GPT-Rosalind, which is custom-built for scientists working on drug discovery, biology and other medical research. Named for Rosalind Franklin, who helped uncover the structure of DNA, the system is aimed at helping to speed up the R&D process. The news follows an earlier announcement of a strategic partnership between the company and Ozempic maker Novo Nordisk, which plans to integrate more AI across its business units.

The new model works as a one-stop interface shop, connecting more than 50 tools used by scientists day-to-day, such as journal articles, molecule databases and predictive tools. It’s also integrated into Codex, the company’s AI coding assistant, to help enable custom workflows.

Life sciences have become a major area for competition in the AI industry, and OpenAI is far from the only player. Nvidia has a number of different platforms for researchers, as does Anthropic with its Claude for Life Sciences. And earlier this week, Amazon launched its own product, Amazon Bio Discovery, specifically aimed at integrating lab work with AI models.

And although GPT-Rosalind is new, OpenAI has been working with scientists for years. Derya Unutmaz, an immunologist who works at biomedical research organization Jackson Laboratory, has been using ChatGPT since version 3.5 came out.

He’s undeniably enthusiastic about its potential, and said that more recent versions have helped him and other scientists in his lab to better understand different immunological mechanisms, which guided their experiments. He’s also using it to help him write a textbook on how T-cells works, and has praised the model’s accuracy.

Jason Kelly, CEO of Ginkgo Bioworks, is also optimistic about using AI for scientific research. He said that his company has integrated OpenAI’s models into its physical laboratory setups, so that the AI can automatically design, execute and iterate scientific experiments. It’s also integrated agents into its Cloud Lab, which allows scientist-customers to use it as an interface to help design and direct experiments.

For Kelly, the most exciting potential of AI is a future where every scientist is essentially running their own lab, directing AI agents to do experiments for them. “It’s going to be that you come in in the morning, have your coffee, and look at the data from your experiments the night before,” he said, rather than manually putting things into test tubes or cell cultures.

this often happens in small, compounding ways, not necessarily all in one swoop. “If we can help the world do the next 25 years of science in five instead, we could be sitting here in 2030 with the tools that we would have otherwise had in 2050,” he said. “That’s an awesome place to be.”