Summary of Reinforcement Learning For Sequence Design Leveraging Protein Language Models, by Jithendaraa Subramanian et al.
Reinforcement Learning for Sequence Design Leveraging Protein Language Models
by Jithendaraa Subramanian, Shivakanth Sujit, Niloy Irtisam, Umong Sain, Riashat Islam, Derek Nowrouzezahrai, Samira Ebrahimi Kahou
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach to protein sequence design using reinforcement learning (RL) and protein language models (PLMs). Prior methods have relied on evolutionary strategies or Monte-Carlo methods, but these approaches often fail to exploit the structure of the combinatorial search space. By leveraging PLMs as a reward function, the authors aim to generate novel sequences that are biologically plausible. To address the computational expense of querying the large PLM, they propose using a smaller proxy model that is periodically finetuned. The paper presents extensive experiments on various sequence lengths, demonstrating favorable evaluations and high diversity scores for the proposed sequences. The authors also provide a modular open-source implementation that can be easily integrated into RL training loops. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create new proteins by learning from big collections of protein information. Currently, we use methods like evolution or random guessing to design proteins, but these approaches don’t always work well. The researchers propose a new way using “reinforcement learning” and special language models that understand proteins. This approach tries to generate new protein sequences that are likely to work in the body. To make it faster and more efficient, they use a smaller model that gets updated periodically. They tested this method on different-sized protein sequences and found that it works well, producing diverse and biologically plausible results. The code for all their experiments is available online. |
Keywords
* Artificial intelligence * Reinforcement learning
