The Unreasonable Redundancy of Nature's Protein Folds

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. In the near future, we might see most antibodies entering the clinic designed in large part with deep-learning-based generative models, potentially with superior pharmaceutical properties and targeting receptors that have resisted wet-lab based approaches.