Summary of Active Learning For Affinity Prediction Of Antibodies, by Alexandra Gessner et al.
Active learning for affinity prediction of antibodies
by Alexandra Gessner, Sebastian W. Ober, Owen Vickery, Dino Oglić, Talip Uçar
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
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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 active learning framework is proposed to accelerate the identification of mutations that enhance antibody binding affinity, leveraging relative binding free energy (RBFE) methods and simulator evaluations. This approach iteratively suggests promising sequences for computational simulation, reducing the reliance on costly wet-lab experiments. The framework explores different modeling approaches to identify the most effective surrogate model, demonstrating its effectiveness in both pre-computed data pools and realistic full-loop settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to quickly find better medicines by looking at how different parts of a molecule stick together. This helps scientists pick the best ideas without needing to do lots of tests in a lab. The method uses special computer programs that look at how well different pieces fit together, and it works pretty well! |
Keywords
» Artificial intelligence » Active learning
