GoKawiilIn a new study, Apple researchers detail SimpleDesign, a streamlined AI model that can jointly generate protein sequences and structures. Here are the details.
A bit of context
Last September, Apple researchers published a study titled “SimpleFold: Folding Proteins is Simpler than You Think,” which detailed a streamlined approach to predicting a protein’s 3D structure from its amino acid sequence.
In a nutshell, SimpleFold uses a flow-matching model to generate a protein’s 3D structure directly from its amino acid sequence.
We explained flow matching in more detail here, but the short of it is that the technique starts with a noisy, random basis and learns a relatively direct path toward the final result. That contrasts with diffusion models, which typically work by iteratively removing noise until they reach the final output.
Both techniques are most commonly (or at least historically) associated with image generation, although researchers (including those at Apple) have also explored diffusion models for text and code generation.
Back to SimpleFold, Apple basically paired flow matching with general-purpose Transformer blocks (commonly used in text generation), allowing the model to avoid some of the more computationally expensive techniques typically used by protein-folding models, such as DeepMind’s famous AlphaFold.
Now, Apple researchers have unveiled SimpleDesign, which applies the same push toward simpler, general-purpose architectures explored with SimpleFold to the broader problem of protein design, rather than just predicting a protein’s 3D structure.
SimpleDesign
As Apple’s researchers explain in a new study titled “SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign”:
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