Summary of From Words to Molecules: a Survey Of Large Language Models in Chemistry, by Chang Liao et al.
From Words to Molecules: A Survey of Large Language Models in Chemistry
by Chang Liao, Yemin Yu, Yu Mei, Ying Wei
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper explores the intersection of Large Language Models (LLMs) and chemistry, highlighting the complexities and innovations that arise from integrating these two fields. The authors examine how molecular information is fed into LLMs through various representation and tokenization methods, and categorize chemical LLMs based on their input data. They discuss pretraining objectives adapted for chemical LLMs and explore diverse applications in chemistry tasks, including novel paradigms for applying LLMs to chemical problems. The paper concludes by identifying promising research directions, such as integrating LLMs with chemical knowledge, advancements in continual learning, and improvements in model interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can learn about chemistry using big language models. It’s hard to get these models to understand chemistry because they’re designed for languages like English. The authors figure out how to make the models work better by changing how they see molecules and categorizing them into different types. They also talk about what makes these models good at doing chemical tasks, like predicting reactions. Finally, they suggest new areas of research that could lead to big breakthroughs in chemistry. |
Keywords
* Artificial intelligence * Continual learning * Pretraining * Tokenization
