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MolBasic Improves Molecular LLMs with SMILES-Graph Translation

Wenda Wang, Jinjia Feng, Zhewei Wei· July 7, 2026 View original

Key takeaways

  • Molecular LLMs often lack fundamental structural understanding from SMILES strings.
  • MolBasic improves this via bidirectional SMILES-Graph translation.
  • A progressive learning scheme with Chain-of-Thought enhances molecular reasoning.
  • This structure-first approach leads to robust gains in downstream molecular tasks.

Who benefits

PharmaceuticalsBiotechnologyChemical ManufacturingMaterials ScienceAgri-food

Summary

MolBasic is a structure-first framework that enhances molecular understanding in LLMs by strengthening their grasp of molecular graphs from SMILES strings through bidirectional SMILES-Graph translation. This approach improves basic structure recognition and downstream tasks like property prediction.

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.

Why it matters

For professionals in computational chemistry, drug discovery, and materials science, MolBasic offers a crucial method to build more reliable and chemically-grounded molecular LLMs. This leads to more accurate predictions and more effective design of new molecules.

How to implement this in your domain

  1. 1Integrate SMILES-Graph translation tasks into the pre-training or fine-tuning of molecular LLMs.
  2. 2Develop or adopt multi-level structure perception benchmarks to evaluate molecular LLM understanding.
  3. 3Apply a progressive learning scheme with Chain-of-Thought prompting for molecular reasoning tasks.
  4. 4Utilize MolBasic-enhanced LLMs for improved property prediction and molecular optimization in R&D.

Original post by Wenda Wang, Jinjia Feng, Zhewei Wei

"arXiv:2607.03007v1 Announce Type: new 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…"

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Originally posted by Wenda Wang, Jinjia Feng, Zhewei Wei on X · view source

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