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，分子数据正在迅速扩展，系统复杂性呈指数级增长，大型语言模型（LLMs）和基于代理的系统从根本上重塑了这一领域。 这篇观点文章考察了LLMs、智能代理和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），这是一种创新的多代理LLM基础框架。 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。 它采用了一种混合神经符号架构，结合了由LLM驱动的语义工具与确定性的符号计算。 此外，它的ADAM工具协议（ATP）支持异步、以数据库为中心的工具编排，促进了社区驱动的可扩展性。 尽管已经取得了显著进展，但持续存在的挑战要求进一步努力建立基准标准，优化基础模型和代理，并构建一个开放的合作生态系统。 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, and building an open collaborative ecosystem. ADAM is accessible at https://sidereus-ai.com.