用光谱信息训练分子模型，实现从谱图反推结构、生成三维构象。

Large language models have emerged as transformative tools in molecular science, demonstrating remarkable potential in molecular property prediction and de novo molecular design. However, their application to spectroscopy remains notably limited, despite its foundational role in experimental molecular characterization and structural validation. Progress in spectroscopy-grounded reasoning has been hindered by the lack of standardized spectral representations and comprehensive evaluation protocols, making cross-study comparisons difficult. To bridge this gap, we present a unified framework for spectroscopy-grounded molecular modeling and evaluation. At its core, the SpecMol foundation model integrates spectral interpretation, molecular representation learning, and three-dimensional structure generation within a single interface. Complementing this, we establish SpecMol-Bench as a systematic evaluation protocol encompassing cross-modal tasks: spectra-to-structure elucidation, structure-to-spectra simulation, and SMILES-to-3D conformation generation. Under this unified framework, SpecMol achieves accurate spectra-driven structure elucidation and reproduces experimental nuclear magnetic resonance characteristics with high fidelity. The model also generates chemically valid three-dimensional conformations directly from SMILES strings and consistently outperforms existing general-purpose molecular language models across standardized evaluation metrics. Code is available at https://github.com/Eurekashen/SpecMol