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SimpleDesign: A Joint Model for Protein Sequence and Structure Codesign
Summary
SimpleDesign addresses protein design as a joint modeling problem involving amino acid sequences and three-dimensional structures, whose interplay determines protein function. Unlike approaches that first train autoencoders and then model their latent representations, it uses a single-stage, end-to-end objective directly in data space. The objective combines discrete cross-entropy for sequences with a regression objective for structures, while Transformer-based multimodal backbones provide modality-specific processing and global self-attention across both modalities. Trained on more than 2M sequence-structure pairs, SimpleDesign achieves competitive performance on co-design and unconditional sequence and structure generation benchmarks. The work argues, based on these results, that multi-stage training is not necessary for performant co-design models.
Context
Protein function depends on the complex interplay between amino acid sequence and three-dimensional structure. Existing generative approaches often use multi-stage training: autoencoders first tokenize data into latent representations, followed by generative modeling in the resulting latent space.
Approach / What changed
SimpleDesign is trained directly in data space with a single-stage, end-to-end objective combining discrete cross-entropy for sequences and regression for structures. Transformer-based multimodal backbones provide modality-specific processing while retaining global self-attention across both modalities.
Takeaways
SimpleDesign models protein sequences and structures jointly rather than training separate latent-space stages.
Its training objective combines sequence cross-entropy with structure regression, reflecting the two modalities’ different representations.
Training on more than 2M sequence-structure pairs produced competitive results on co-design and unconditional sequence and structure generation benchmarks.
Source and freshness
- Source
- machinelearning.apple.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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