2025-01-10 パシフィック・ノースウェスト国立研究所 (PNNL)
＜関連情報＞
- https://www.pnnl.gov/publications/new-ai-agent-connects-computer-reasoning-chemistry
- https://pubs.acs.org/doi/full/10.1021/acsomega.4c08408
CACTUS：道具の使い方を科学につなげる化学エージェント CACTUS: Chemistry Agent Connecting Tool Usage to Science
Andrew D. McNaughton,Gautham Krishna Sankar Ramalaxmi,Agustin Kruel,Carter R. Knutson,Rohith A. Varikoti, and Neeraj Kumar
ACS Omega Published: October 25, 2024
DOI:https://doi.org/10.1021/acsomega.4c08408
Abstract
Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.
