Reinforcement learning: From board games to protein design
Reinforcement learning: From board games to protein design
Reinforcement learning: From board games to protein design
By Ian Haydon
April 20, 2023
Summary
The team of researchers developed powerful new protein design software adapted from a strategy proven adept at board games like Chess and Go. In one experiment, proteins made with the new approach were found to be more effective at generating useful antibodies in mice.
"Our results show that reinforcement learning can do more than master board games. When trained to solve long-standing puzzles in protein science, the software excelled at creating useful molecules," said senior author David Baker, professor of biochemistry at the UW School of Medicine in Seattle and a recipient of the 2021 Breakthrough Prize in Life Sciences.
To make a reinforcement learning program for protein design, the scientists gave the computer millions of simple starting molecules.
Their team's Science manuscript is titled "Top-down design of protein architectures with reinforcement learning."
"Our approach is unique because we use reinforcement learning to solve the problem of creating protein shapes that fit together like pieces of a puzzle," explained co-lead author Lutz, a doctoral student at the UW Medicine Institute for Protein Design.
Reference
Haydon, I. (2023, April 20). Reinforcement learning: From board games to protein design. Phys.org. https://phys.org/news/2023-04-board-games-protein.html