MolBasic Improves Molecular LLMs with SMILES-Graph Translation

Despite recent advancements, molecular large language models (LLMs) often struggle with fundamental structural understanding, failing to accurately capture molecular graphs from canonical SMILES representations. This contradicts the core chemistry principle that structure dictates function. This research introduces MolBasic, a "structure-first" framework to address this deficiency. MolBasic centers on a multi-level structure perception benchmark, where the core task is bidirectional SMILES-Graph conversion. This process aims to align the sequential (SMILES) and topological (graph) representations of molecules within the LLM. Building on this foundation, MolBasic employs a progressive learning scheme combined with a standardized Chain-of-Thought (CoT) approach. This guides models from basic structure acquisition towards more complex molecular reasoning. Experiments demonstrate that MolBasic significantly improves structural understanding and yields robust gains on downstream tasks such as property prediction and objective optimization, validating its structure-first paradigm.