Imagine being able to speed up evolution– hypothetically – to learn which genes might have a harmful or beneficial effect on human health.
Imagine being able to speed up evolution– hypothetically – to learn which genes might have a harmful or beneficial effect on human health. Imagine, further, being able to rapidly generate new genetic sequences that could help cure disease or solve environmental challenges. Now, scientists have developed a generative AI tool that can predict the form and function of proteins coded in the DNA of all domains of life, identify molecules that could be useful for bioengineering and medicine, and allow labs to run dozens of other standard experiments with a virtual query – in minutes or hours instead of years (or millennia).
The open-source, all-access tool, known as Evo 2, was developed by a multi-institutional team co-led by Stanford’s Brian Hie, an assistant professor of chemical engineering and a faculty fellow in Stanford Data Science. Evo 2 was trained on a dataset that includes all known living species, including humans, plants, bacteria, amoebas, and even a few extinct species. Stanford Report talked to Hie about Evo 2’s advanced capabilities, why the scientific world is so eager to get its hands on this new tool, and how Evo 2 could reshape the biological sciences.
Read more at: Stanford University
From left to right: Michael Poli, Brian Hie, and Garyk Brixi. Biology is written in a combination of As, Cs, Gs, and Ts that can be hard to understand. The Evo2 team, co-led by Assistant Professor Brian Hie, aims to make the language of biology more accessible to researchers. (Photo Credit: Andrew Brodhead)
