The Unreasonable Redundancy of Nature's Protein Folds
Recent advancements in deep learning have significantly improved the generative modeling of biomolecules, particularly through models like AlphaFold3. These models facilitate the prediction of biomolecular interactions and the design of drug-like molecules. However, challenges remain in achieving structural diversity due to the redundancy of natural protein folds.
- ▪Deep neural networks have enhanced generative language modeling and are now being applied to biomolecular modeling.
- ▪AlphaFold3 has made it easier to predict biomolecular interactions and design drug-like molecules.
- ▪The redundancy of natural protein folds poses challenges for achieving structural diversity in biomolecular models.
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| Original publisher | Ligo |
| Canonical URL | https://research.ligo.bio/posts/unreasonable-redundancy-of-natural-protein-folds/ |
| Publication time | Wed, 03 Jun 2026 03:47:56 +0000 |
| Retrieval time | 2026-06-03T04:16:54.537Z |
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Opening excerpt (first ~120 words) tap to expand
The Unreasonable Redundancy of Nature's Protein Folds Arda Goreci · May 20, 2026 Over the last few years, deep neural networks have made generative language modeling dramatically more powerful, giving us large language models. A similar leap happened for continuous modalities like images and videos. Recently, similar techniques have been applied to the generative modeling of biomolecules with great success. Models such as DeepMind's AlphaFold3 made it much easier to predict biomolecular interactions, including drug-protein and antibody-protein complexes, and soon after people figured out how to re-purpose these capabilities to design drug-like molecules. Chai-2, Latent-X2, and Nabla all report developable antibody or biologics designs.
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