Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics
Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics
In computational biophysics, where molecular data is expanding rapidly and system complexity is increasing exponentially, large language models (LLMs) and agent-based systems are fundamentally reshaping the field. This perspective article examines the recent advances at the intersection of LLMs, intelligent agents, and scientific computation, with a focus on biophysical computation. Building on these advancements, we introduce ADAM (Agent for Digital Atoms and Molecules), an innovative multi-agent LLM-based framework. ADAM employs cutting-edge AI architectures to reshape scientific workflows through a modular design. It adopts a hybrid neural-symbolic architecture that combines LLM-driven semantic tools with deterministic symbolic computations. Moreover, its ADAM Tool Protocol (ATP) enables asynchronous, database-centric tool orchestration, fostering community-driven extensibility. Despite the significant progress made, ongoing challenges call for further efforts in establishing benchmarking standards, optimizing foundational models and agents, building an open collaborative ecosystem and developing personalized memory modules. ADAM is accessible at https://sidereus-ai.com.
Yijie Xia、Xiaohan Lin、Zicheng Ma、Jinyuan Hu、Yanheng Li、Zhaoxin Xie、Hao Li、Li Yang、Zhiqiang Zhao、Lijiang Yang、Zhenyu Chen、Yi Qin Gao
生物物理学计算技术、计算机技术
Yijie Xia,Xiaohan Lin,Zicheng Ma,Jinyuan Hu,Yanheng Li,Zhaoxin Xie,Hao Li,Li Yang,Zhiqiang Zhao,Lijiang Yang,Zhenyu Chen,Yi Qin Gao.Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics[EB/OL].(2025-04-30)[2025-06-10].https://arxiv.org/abs/2505.00270.点此复制
评论