Summary of Harnessing Preference Optimisation in Protein Lms For Hit Maturation in Cell Therapy, by Katarzyna Janocha et al.
Harnessing Preference Optimisation in Protein LMs for Hit Maturation in Cell Therapy
by Katarzyna Janocha, Annabel Ling, Alice Godson, Yulia Lampi, Simon Bornschein, Nils Y. Hammerla
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 machine learning-based approach fine-tunes protein language models using a high-throughput experimental platform, generating datasets suitable for immunotherapy applications. The model demonstrates surprising correlations with biological assays, enabling few-shot hit maturation in CARs. This proof-of-concept presents a novel pathway for applying ML to immunotherapy and could generalize to other therapeutic modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers used machine learning to improve protein language models by fine-tuning them on data from a high-throughput experimental platform. The results showed that the models worked well with biological assays, which is important for developing new treatments like CARs (Chimeric Antigen Receptors) for cancer and autoimmune disorders. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Machine learning
