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New agentic LLM S3C-LLM enhances molecular structure elucidation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed S3C-LLM, a novel agentic language model designed for spectrum-to-structure elucidation in molecular analysis. Unlike previous methods that directly convert spectra to SMILES, S3C-LLM mimics the analytical process of spectroscopists by retrieving specific skills, executing analysis code, and integrating evidence before generating the molecular structure. This approach, trained using a supervised fine-tuning and reinforcement learning strategy on the Qwen3-4B model, demonstrates superior performance compared to existing general and spectrum-specific models. AI

IMPACT This model could improve the accuracy and efficiency of molecular analysis in chemistry and drug discovery.

RANK_REASON The cluster describes a new research paper detailing a novel model for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New agentic LLM S3C-LLM enhances molecular structure elucidation

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The cluster describes a new research paper detailing a novel model for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Xuanle Zhao, Xinyuan Cai, Xiang Cheng, Bo Xu ·

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

    arXiv:2608.30910v1 Announce Type: cross Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct spectrum-to-SMILES generation. Although this paradigm can leverage paired spec…

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