Analyzing Redundancy in Natural Protein Fold Space and the Role of Generative AI in Biomolecular Modeling
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Summary
This research note from Ligo explores the concept of redundancy in natural protein fold space, examining how deep neural networks and generative models like AlphaFold3 have revolutionized biomolecular interaction prediction. The article analyzes which types of protein fold data still carry meaningful signal versus what is redundant, drawing parallels to advances in large language models and generative AI for continuous modalities like images and video.
Key quotes
· 4 pulledOver 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 interactions.
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