Back to Basics: Improving Molecular Understanding in LLMs via SMILES-Graph Translation

2026-07-03

AI Summary

This work addresses a critical limitation in current molecular large language models—their inadequate adherence to the principle that “structure determines function,” resulting in weak foundational structural understanding. To overcome this, the authors propose MolBasic, a novel framework that introduces a structure-first paradigm centered on bidirectional translation between SMILES strings and molecular graphs. By aligning topological and sequential representations through a multi-level structure-aware benchmark, and integrating progressive learning with standardized chain-of-thought prompting, MolBasic enhances the model’s capacity to evolve from basic structural perception to advanced reasoning. The approach achieves substantial improvements in structural comprehension and consistently delivers robust gains across downstream tasks, including molecular property prediction and target-oriented molecular optimization.

Abstract

Recent advances in molecular large language models have led to strong performance on molecular understanding and generation tasks, yet these gains often come without reliable structural grounding. In particular, existing approaches conflict with the chemistry principle that structure determines function: despite their downstream success, current molecular LLMs perform poorly on basic structure recognition, suggesting that they fail to capture molecular graphs from canonical SMILES. To remedy this, we propose MolBasic, a structure-first framework that strengthens structural comprehension via SMILES-Graph translation. MolBasic is built around a multi-level structure perception benchmark, where bidirectional SMILES-Graph conversion serves as the core task to align sequential and topological representations. On top of this foundation, we employ a progressive learning scheme with a standardized Chain-of-Thought (CoT) to steer models from structure acquisition toward higher-level molecular reasoning. Experiments show that MolBasic substantially improves structural understanding and yields robust gains on downstream tasks, including property prediction and objective optimization, supporting our structure-first paradigm.

Problem

• molecular understanding
• SMILES
• molecular graph
• structural grounding
• large language models

Innovation

• SMILES-Graph translation
• structure-first paradigm
• molecular large language models
• Chain-of-Thought reasoning
• structural comprehension