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The paper introduces SimpleDesign, a model intended to jointly model protein amino-acid sequences and three-dimensional structures to support co-design. It frames this approach as an alternative to common multi-stage pipelines that first train autoencoders to produce latent tokens and then train generative models on those latents.
Researchers describe a new machine‑learning framework intended to improve the success rate of computational protein design by generating solutions that do not merely reproduce sequences observed in nature. The approach is presented as a way to explore sequence space beyond natural examples while aiming to raise the proportion of designs that fold or function as intended.
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