MolGoT: Organizing LLM Molecular Reasoning with Structure-Guided Graphs of Thought

Large language models (LLMs) are increasingly used to understand, modify, and generate molecules through natural-language instructions. Recent approaches expose atom identities and bond relations in explicit molecular representations, reducing the need to reconstruct connectivity from compact string syntax before performing chemical operations. However, making structure accessible does not determine how intermediate decisions should be coordinated: observations from connected regions may depend on one another, while separately proposed edits can interact when combined. Graph of Thoughts offers a framework for this coordination by representing intermediate results as nodes and their dependencies as edges, enabling findings from different reasoning paths to be combined and revised. Molecular structure can ground these operations by identifying which atoms and regions each state concerns and where their consequences may interact. Building on this connection, we introduce MolGoT, a training-free method that retains SMILES and organizes molecular reasoning through structure-guided graphs of thought. MolGoT constructs a shared structural context and maintains local evidence and executable molecular candidates, with task-specific evaluation, integration, and revision for molecular understanding, analysis, editing, and generation. Across eleven molecular tasks, MolGoT achieves the highest average accuracy among the four evaluated pipelines. These results suggest that structure-guided thought graphs provide a controllable framework for improving both the accuracy and token efficiency of molecular reasoning in LLMs.