Summary of Diffusion on Language Model Encodings For Protein Sequence Generation, by Viacheslav Meshchaninov et al.
Diffusion on language model encodings for protein sequence generation
by Viacheslav Meshchaninov, Pavel Strashnov, Andrey Shevtsov, Fedor Nikolaev, Nikita Ivanisenko, Olga Kardymon, Dmitry Vetrov
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel latent diffusion framework, DiMA, is introduced for protein sequence design. By operating on protein language model representations, DiMA achieves consistently high performance across multiple protein encoders, ranging from 8M to 3B parameters. The framework demonstrates strong results in generating novel, high-quality, and diverse protein sequences, outperforming baselines such as autoregressive, discrete diffusion, and flow matching language models. DiMA supports conditional generation tasks like protein family-generation, motif scaffolding, and infilling, and fold-specific sequence design. This work provides a universal continuous diffusion framework for protein sequence generation, offering architectural insights and practical applicability across various protein design scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiMA is a new way to create protein sequences using language models. It works by building on the representations of proteins created by these models. The results show that DiMA can generate high-quality, diverse, and novel protein sequences better than other methods like autoregressive or discrete diffusion. This framework can be used for different tasks such as generating entire protein families or filling in gaps in known proteins. |
Keywords
* Artificial intelligence * Autoregressive * Diffusion * Language model
