Summary of Bayesian Optimization Of Antibodies Informed by a Generative Model Of Evolving Sequences, By Alan Nawzad Amin et al.
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
by Alan Nawzad Amin, Nate Gruver, Yilun Kuang, Lily Li, Hunter Elliott, Calvin McCarter, Aniruddh Raghu, Peyton Greenside, Andrew Gordon Wilson
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 This paper introduces Clone-informed Bayesian Optimization (CloneBO), a novel method for designing effective therapeutics by optimizing antibody sequences. The approach leverages the human immune system’s natural optimization process, where it iteratively evolves specific portions of antibody sequences to bind targets strongly and stably. A large language model, CloneLM, is trained on hundreds of thousands of clonal families to predict typical antibodies. CloneBO combines this generative model with a Bayesian optimization procedure to efficiently optimize antibodies in the lab. The authors demonstrate that CloneBO outperforms previous methods in both simulated and real-world experiments, designing stronger and more stable binders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us design better medicine by understanding how our bodies create strong antibodies. Right now, finding good antibody sequences is like searching a huge library for the right book. Scientists try to guess which sequence will work best, but it’s hard to predict. The authors of this paper created a new way to find good antibody sequences using a big computer model that learns from lots of examples. This model can even look at how our bodies create antibodies and use that knowledge to design better ones. The results show that their method is much faster and more effective than previous methods, which could lead to breakthroughs in medicine. |
Keywords
» Artificial intelligence » Generative model » Large language model » Optimization
