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Cheminformatics

S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation

Xuanle Zhao, Xinyuan Cai, Xiang Cheng, Bo Xu

Featured September 5, 2026

Original Paper

AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

Instead of guessing a molecule directly from its spectrum, this new AI acts like a smart chemist, using specialized skills and running code to carefully analyze spectral clues step-by-step before figuring out the final molecular structure.

In depth
The paper introduces S3C-LLM, an agentic Large Language Model that reformulates spectrum-to-structure prediction. Instead of direct generation, it employs a skill-guided and code-grounded reasoning process. This involves retrieving modality-specific spectroscopy skills, executing analysis code to extract peak-level evidence and formula constraints, and then integrating this explicit evidence before generating the molecular SMILES.

Key Takeaways

  • 1
    S3C-LLM leverages a self-evolving spectroscopy skill library and executable analysis code to mimic human expert reasoning for molecular structure elucidation.
  • 2
    The model is trained using a two-stage strategy combining supervised fine-tuning (SFT) with novel step-level reinforcement learning (RL) to optimize intermediate reasoning steps.
  • 3
    This agentic approach achieves state-of-the-art performance across diverse spectral benchmarks, demonstrating high data efficiency compared to prior LLM-based methods.

Conceptual Flow

HIGH LEVEL
1
How the AI Thinks Like a Chemist

The AI first learns chemical rules, then uses these rules to run small programs that analyze spectrum data, and finally combines everything to guess the molecule.

Raw Spectrum Data
Use Chemical Rules + Run Code
Step-by-Step Analysis
Final Molecule Guess
2
Better Molecule Predictions with Less Data

This new AI finds molecules more accurately than older methods, even when given much less training data, making it very efficient.

Old AI Performance
New AI Performance
Compare Accuracy & Data Needs
New AI is More Accurate
New AI Needs Less Data

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