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An AI strategy proven adept at board games like Chess and Go, reinforcement learning, has now been adapted for a powerful protein design program. The 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. In one experiment, proteins made with the new approach were found to be more effective at generating useful antibodies in mice than were previous methods. If this method is applied to the right research problems, it likely could accelerate progress in a variety of scientific fields.
A new protein nanoparticle modeling application that removes hidden transmembrane sequences has just been introduced by researchers at the University of Washington.