Fine-tuning large language models for chemical text mining-Reference-Cited by-同舟云学术

Fine-tuning large language models for chemical text mining

Published:2024 Issue:27 Volume:15 Page:10600-10611
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Zhang Wei¹²,Wang Qinggong³,Kong Xiangtai¹²,Xiong Jiacheng¹²,Ni Shengkun¹²,Cao Duanhua¹⁴,Niu Buying¹²,Chen Mingan¹⁵⁶,Li Yameng⁷,Zhang Runze¹²,Wang Yitian¹²,Zhang Lehan¹²,Li Xutong¹²,Xiong Zhaoping⁷,Shi Qian⁶,Huang Ziming⁸,Fu Zunyun¹,Zheng Mingyue¹²³^ORCID

Affiliation:

1. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China

2. University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China

3. Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China

4. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China

5. School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China

6. Lingang Laboratory, Shanghai 200031, China

7. ProtonUnfold Technology Co., Ltd, Suzhou, China

8. Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany

Abstract

Extracting knowledge from complex chemical texts is essential for both experimental and computational chemists. Fine-tuned large language models (LLMs) can serve as flexible and effective extractors for automated data acquisition.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D4SC00924J

Reference35 articles.

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