Large Language Models for Molecular Science: A Survey on Representation and Cognition
Year: 2026
Type: preprint
Abstract: The integration of Large Language Models (LLMs) into the natural sciences marks a shift from specialized discriminative models to generalist generative agents. However, adapting LLMs to chemistry presents a core challenge: the mismatch between the linear, ambiguous nature of natural language and the discrete, topological, and physical nature of molecules. This review provides a systematic framework for bridging this gap, organizing recent advances through the dual lenses of representation alignment and cognitive mechanism. First, we examine how molecular modalities are adapted to the transformer architecture, progressing from Linguistic Linearization, which optimizes symbolic grammars such as SMILES and SELFIES, to Topological Perception, which injects graph inductive biases through adapters and projectors, and finally to Geometric Grounding, which encodes 3D physical reality and equivariance. Second, we analyze how LLMs acquire and deploy chemical knowledge through three complementary pathways: Internalization, which cultivates parametric intuition through pre-training and alignment; Externalization, which anchors generation to physical reality via retrieval and tool orchestration; and Reasoning, which we further decompose into four strategies spanning contextual analogy, sequential deduction, strategic planning, and introspective refinement. Building on this dual-lens framework, we map downstream capabilities onto a four-level task hierarchy spanning Semantic Translation, Predictive Inference, Constrained Generation, and Autonomous Discovery, and we identify three coupled challenges at the representation-cognition interface, outlining a research roadmap toward chemically grounded AI systems.
Source: ChemRxiv
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204280403