A Review of Large Language Models and Autonomous Agents in Chemistry
arxiv(2024)
摘要
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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