Molecular LLM Agents: From Architectural Design to Scientific Autonomy

2026-08-24

AI Summary

本文提出了一种分子LLM代理的概念框架，从分子表示、感知到科学自主性层级，旨在解决分子科学中代理设计和部署的问题。

Abstract

Molecular science represents an important frontier for LLM-based agents. Unlike general agents that mainly operate over natural language, code, or web environments, molecular LLM agents must perceive, reason about, and act upon chemical objects across symbolic strings, molecular graphs, 3D conformations, spectra, simulations, and wet-lab measurements. Their capabilities depend on chemically faithful molecular perception, an LLM-centered agent framework, domain-specific tool grounding, and computational or experimental feedback, in addition to planning and tool use. This work develops a conceptual framework for molecular LLM agents from two complementary perspectives. First, we introduce an architectural view of molecular-agent design, covering molecular representation and perception, the agent framework, domain-specific toolboxes, and learning and optimization. Second, we propose a scientific autonomy ladder inspired by staged autonomy in engineering systems, categorizing agents into four levels: L1 assistive or fixed workflows, L2 adaptive computational agents, L3 feedback-aware physical experiment agents, and L4 scientific-agenda agents. Together, these two perspectives establish a comprehensive framework for comparing existing molecular LLM agents, identifying missing capabilities and deployment risks, and guiding the design, evaluation, and deployment of future agents in molecular discovery workflows.

Problem

• molecular LLM agents
• chemical objects
• molecular perception

Innovation

• molecular LLM agents
• scientific autonomy ladder
• molecular representation and perception
• domain-specific toolboxes
• feedback-aware physical experiment