标题： 大型语言模型作为数字原子和分子的AI代理：催化计算生物物理学的新时代 显示英文标题

标题： Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics

摘要： 在计算生物物理学领域，随着分子数据的快速扩展和系统复杂性的指数增长，大型语言模型（LLMs）和基于代理的系统正在从根本上重塑该领域。 本文综述了LLMs、智能代理与科学计算交叉领域的最新进展，重点放在生物物理计算上。 基于这些进展，我们介绍了ADAM（数字原子和分子代理），这是一种创新的基于多代理LLM的框架。 ADAM采用前沿的人工智能架构，通过模块化设计重塑科学工作流程。 它采用混合神经符号架构，结合了LLM驱动的语义工具与确定性符号计算。 此外，其ADAM工具协议（ATP）实现了异步、以数据库为中心的工具编排，促进了社区驱动的可扩展性。 尽管取得了显著进展，但持续存在的挑战需要进一步努力来建立基准标准、优化基础模型和代理、构建开放协作生态系统以及开发个性化记忆模块。 ADAM可在https://sidereus-ai.com访问。 显示英文摘要

摘要： 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.