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A protein-design model aims to turn natural-language instructions into structures

A researcher says it better targets specific protein forms and functions and works especially well for agent-driven design.

Pang Wei KohPW
1 Source, 53m ago, first seen 53m ago

TLDR

A researcher describes a year-long collaboration that produced a model they say turns natural-language instructions into corresponding protein structures. They contrast it with existing protein-design models, which they say are limited in what inputs can guide their output. They liken the difference to image generation controlled by class labels versus natural-language prompts, and say the model is more effective at targeting particular protein forms and functions.

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1 Source, first seen 53m ago

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1 Source, first seen 53m ago

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1 Source

Pang Wei Koh@PangWeiKohSuper excited to share our year-long work on designing proteins directly from natural language, led by @jleechung in collaboration with @UWproteindesign @allen_ai! Current protein design models are constrained in what they can condition on. As an analogy, think of an image generation model that only conditions on discrete class labels like "dog" or "cat", or "outdoors" or "indoors"; in contrast, a modern text-to-image model can be much more expressively controlled. We do the same for protein design here, and the resulting model natively turns natural instructions into corresponding protein structures. This makes it much more effective at designing proteins with particular form/function, and it works especially well for agent-driven protein design!53m
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    1 Source

    Pang Wei Koh@PangWeiKohSuper excited to share our year-long work on designing proteins directly from natural language, led by @jleechung in collaboration with @UWproteindesign @allen_ai! Current protein design models are constrained in what they can condition on. As an analogy, think of an image generation model that only conditions on discrete class labels like "dog" or "cat", or "outdoors" or "indoors"; in contrast, a modern text-to-image model can be much more expressively controlled. We do the same for protein design here, and the resulting model natively turns natural instructions into corresponding protein structures. This makes it much more effective at designing proteins with particular form/function, and it works especially well for agent-driven protein design!53m
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