A Review of Large Language Models and Autonomous Agents in Chemistry
- URL: http://arxiv.org/abs/2407.01603v1
- Date: Wed, 26 Jun 2024 17:33:21 GMT
- Title: A Review of Large Language Models and Autonomous Agents in Chemistry
- Authors: Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White,
- Abstract summary: Large language models (LLMs) are emerging as a powerful tool in chemistry across multiple domains.
A core emerging idea is combining LLMs with chemistry-specific tools like synthesis planners and databases, leading to so-called "agents"
An emerging direction is the development of multi-agent systems using a human-in-the-loop approach.
- Score: 0.7184549921674758
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) are emerging as a powerful tool in chemistry across multiple domains. In chemistry, LLMs are able to accurately predict properties, design new molecules, optimize synthesis pathways, and accelerate drug and material discovery. A core emerging idea is combining LLMs with chemistry-specific tools like synthesis planners and databases, leading to so-called "agents." This review covers LLMs' recent history, current capabilities, design, challenges specific to chemistry, and future directions. Particular attention is given to agents and their emergence as a cross-chemistry paradigm. Agents have proven effective in diverse domains of chemistry, but challenges remain. It is unclear if creating domain-specific versus generalist agents and developing autonomous pipelines versus "co-pilot" systems will accelerate chemistry. An emerging direction is the development of multi-agent systems using a human-in-the-loop approach. Due to the incredibly fast development of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.
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