{
  "abstract": "Three-dimensional (3D) molecular structures underpin molecular function and dynamics. Spectroscopy can encode rich structural information via energy-level transitions, but existing data-driven approaches have largely focused on predicting symbolic representations such as SMILES, or molecular topology rather than reconstructing full 3D structures. Here, we introduce SpecDiffMol, the first generative framework to directly reconstruct full molecular 3D structures from molecular formulae and multimodal spectroscopic data, including infrared, Raman, UV–vis, and NMR spectra. SpecDiffMol integrates a Transformer-based topology inference module with an E(3)-equivariant conditional diffusion model, enabling the inference of molecular connectivity, global structure, and fine-grained conformational geometry in a unified framework. Extensive experiments on large-scale theoretical spectroscopic benchmarks comprising about 400,000 molecules systematically elucidate the complementary roles of individual spectroscopic modalities in determining molecular connectivity, backbone topology, and detailed 3D conformations. By explicitly incorporating spectroscopically informed topological priors, the framework substantially constrains the chemical search space and enables robust structure generation. Meanwhile, SpecDiffMol remains effective under realistic experimental conditions. Using automated experimental spectroscopic measurements, we demonstrate its ability to track Z↔E isomerization dynamics in bridged azobenzenes, capturing conformational transitions and key dihedral angle evolution with high accuracy. As a general and extensible approach for spectroscopically driven 3D molecular structure generation, SpecDiffMol opens new avenues for autonomous structure–function discovery and real-time in situ molecular analysis across chemistry, materials science, and molecular biology.",
  "abstract_shown": true,
  "affiliations": [
    "School of Software Engineering",
    "University of Science and Technology of China",
    "Suzhou Institute for Advanced Research, University of Science and Technology of China",
    "Key Laboratory of Precision and Intelligent Chemistry",
    "School of Chemistry and Materials Science",
    "Department of Chemical Engineering and Applied Chemistry",
    "University of Toronto",
    "Department of Chemistry",
    "Vector Institute for Artificial Intelligence"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Wenjie Du"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Xiaohan Qin"
    },
    {
      "affs": [
        3,
        1,
        4
      ],
      "name": "Yixi Shen"
    },
    {
      "affs": [
        3,
        1,
        4
      ],
      "name": "Ledu Wang"
    },
    {
      "affs": [
        5,
        6,
        7,
        8
      ],
      "name": "Andreas Burger"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Chuangwei Liu"
    },
    {
      "affs": [
        3,
        1,
        4
      ],
      "name": "Song Wang"
    },
    {
      "affs": [
        3,
        1,
        4
      ],
      "name": "Jun Jiang"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Yang Wang"
    },
    {
      "name": "Eric Liu",
      "orcid": "0000-0002-8256-6053"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/et2xk068pt0a9.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15011037/v1",
  "fulltext_url": null,
  "id": "et2xk068pt0a9",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011037/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7223675974",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15011037/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/et2xk068pt0a9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15011037",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15011037/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Multimodal Spectroscopy–Driven Reconstruction of Three-Dimensional Molecular Structures via Conditional Diffusion Model",
  "title_text": "Multimodal Spectroscopy–Driven Reconstruction of Three-Dimensional Molecular Structures via Conditional Diffusion Model",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/et2xk068pt0a9",
  "venue": "ChemRxiv",
  "versions": []
}