Link: An interview with Google DeepMind Nobel laureate John Jumper on the creative "off-label" uses for AlphaFold, combining AlphaFold's power with LLMs, and more (Will Douglas Heaven/MIT Technology Review)

John Jumper joined Google DeepMind in 2017 to work on AlphaFold 2, an AI project designed to predict protein structures. Within three years, AlphaFold 2 achieved remarkable accuracy, earning Jumper and his team a Nobel Prize.

AlphaFold 2 rapidly predicted complex structures, solving a 50-year-old grand challenge in biology. It was followed by newer versions that enhanced predictions for multi-protein structures and increased overall speed.

Researchers globally have utilized AlphaFold to explore a variety of scientific problems, from studying disease-resistant proteins in honeybees to enhancing synthetic protein design, marking significant off-label uses.

Despite its success, AlphaFold has limitations, particularly in predicting interactions between multiple proteins, a drawback noted by scientists applying the technology in various fields.

New tools are emerging, building on AlphaFold’s groundwork to advance drug discovery. Innovations like Genesis Molecular AI's Pearl model aim to refine protein interaction predictions, enhancing drug development accuracy.

Jumper envisions blending AlphaFold’s capabilities with large language models to push scientific boundaries further, signaling a promising, if speculative, future for AI in science. #

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