Scientific Large Language Models: A Survey on Biological & Chemical
  Domains
Qiang Zhang Keyang Ding Tianwen Lyv Xinda Wang Qingyu Yin Yiwen Zhang Jing Yu Yuhao Wang Xiaotong Li Zhuoyi Xiang Xiang Zhuang Zeyuan Wang Ming Qin Mengyao Zhang Jinlu Zhang Jiyu Cui Renjun Xu Hongyang Chen Xiaohui Fan Huabin Xing Huajun Chen
Large Language Models (LLMs) have emerged as a transformative power in
enhancing natural language comprehension, representing a significant stride
toward artificial general intelligence. The application of LLMs extends beyond
conventional linguistic boundaries, encompassing specialized linguistic systems
developed within various scientific disciplines. This growing interest has led
to the advent of scientific LLMs, a novel subclass specifically engineered for
facilitating scientific discovery. As a burgeoning area in the community of AI
for Science, scientific LLMs warrant comprehensive exploration. However, a
systematic and up-to-date survey introducing them is currently lacking. In this
paper, we endeavor to methodically delineate the concept of "scientific
language", whilst providing a thorough review of the latest advancements in
scientific LLMs. Given the expansive realm of scientific disciplines, our
analysis adopts a focused lens, concentrating on the biological and chemical
domains. This includes an in-depth examination of LLMs for textual knowledge,
small molecules, macromolecular proteins, genomic sequences, and their
combinations, analyzing them in terms of model architectures, capabilities,
datasets, and evaluation. Finally, we critically examine the prevailing
challenges and point out promising research directions along with the advances
of LLMs. By offering a comprehensive overview of technical developments in this
field, this survey aspires to be an invaluable resource for researchers
navigating the intricate landscape of scientific LLMs.