Summary of Lico: Large Language Models For In-context Molecular Optimization, by Tung Nguyen and Aditya Grover
LICO: Large Language Models for In-Context Molecular Optimization
by Tung Nguyen, Aditya Grover
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM)
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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 The abstract presents a novel approach to optimizing black-box functions by leveraging Large Language Models (LLMs) as surrogate models. The authors introduce LICO, an extended version of arbitrary base LLMs that can perform in-context predictions on diverse sets of functions defined over the molecular domain. This allows for generalization to unseen molecule properties via simple prompting. The approach is evaluated using PMO, a challenging benchmark comprising 20 objective functions, and achieves state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us optimize complex problems by using special machines called Large Language Models (LLMs). These machines are great at recognizing patterns in data, but we need to teach them how to understand specific scientific problems. The scientists developed a new way to do this by adding extra layers to the machine that allows it to make predictions based on examples. This means we can use the machine to solve complex problems without needing a huge amount of training data. The team tested their approach using a set of challenging molecular optimization problems and achieved excellent results. |
Keywords
» Artificial intelligence » Generalization » Optimization » Prompting
