[2509.21861] SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning

Computer Science > Machine Learning arXiv:2509.21861 (cs) [Submitted on 26 Sep 2025 (v1), last revised 20 Mar 2026 (this version, v3)] Title:SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning Authors:Shuaike Shen, Jiaqing Xie, Zhuo Yang, Antong Zhang, Shuzhou Sun, Ben Gao, Tianfan Fu, Biqing Qi, Yuqiang Li View a PDF of the paper titled SpecMol: A Spectroscopy-Grounded Foundation Model for Multi-Task Molecular Learning, by Shuaike Shen and 8 other authors View PDF HTML (experimental) Abstract: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-s...