Summary of Utilizing Large Language Models in An Iterative Paradigm with Domain Feedback For Zero-shot Molecule Optimization, by Khiem Le et al.
Utilizing Large Language Models in an iterative paradigm with domain feedback for zero-shot molecule optimization
by Khiem Le, Nitesh V. Chawla
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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 method, Re²DF, to leverage Large Language Models (LLMs) in molecule optimization for drug discovery. Current approaches show limited performance, but Re²DF iteratively refines the process by providing reliable domain feedback. The feedback is generated using RDKit to handle molecule hallucination and verify if the modified molecule meets the objective. Experiments on single- and multi-property objectives demonstrate significant improvements in Hit ratio, with enhancements of up to 20.76% for single-property objectives and 5.25% for multi-property objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computer models to help scientists create new medicines. The problem is that these computers aren’t very good at this task on their own. The researchers propose a new way of working with the computers to make them better. They use special tools to check if the new molecules are valid and then compare them to what they’re trying to achieve. This helps the computer learn and get closer to creating useful medicines. The results show that this method works well, especially for single tasks where it improved the chances of finding a good medicine by 20%. |
Keywords
* Artificial intelligence * Hallucination * Optimization
