{
  "abstract": "Purification is essential for isolating compounds for reliable characterization and subsequent experiments; however, suitable chromatographic conditions are often still selected through trial and error. Herein, we present a data-driven framework that recommends a stationary phase or separation mode, solvent, and solvent ratio to support thin-layer chromatography (TLC) screening and column chromatography (CC) purification. Using purification records from publications and laboratory notebooks, we developed models to predict TLC and CC conditions. Models using product structures alone or in combination with reactant and agent information were compared across prediction tasks and data sources. Experimental evaluation of 11 previously unreported compounds demonstrated that ranked recommendations could guide TLC screening and the selection of CC purification conditions. Additionally, a web application was developed to provide users with flexible access to the prediction models. The proposed framework supports the selection of practical purification conditions and provides a foundation for integrating purification planning into future autonomous experimental workflows.",
  "abstract_shown": true,
  "affiliations": [
    "Graduate School of Medical Life Science, Yokohama City University, 1-7-29,Suehiro-cho, Yokohama, Kanagawa, Japan, 230-0045",
    "School of Life Science and Technology, Institute of Science Tokyo, 4259 Nagatsuta-cho, Yokohama, Kanagawa, Japan, 226-8501",
    "MolNavi LLC, #402 Wizard Building 1-4-3 Sengen-cho, Yokohama, Kanagawa, Japan, 220-0072",
    "School of Science",
    "Yokohama City University",
    "RIKEN Center for Advanced Intelligence Project, 1-4-1, Nihonbashi,, Tokyo, Japan, 103-0027",
    "Research Center for Autonomous Systems Materialogy (ASMat), Institute of Innovative"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuki Murakami"
    },
    {
      "affs": [
        1
      ],
      "name": "Nobuo Cho"
    },
    {
      "affs": [
        1
      ],
      "name": "Kosei Egami"
    },
    {
      "affs": [
        1
      ],
      "name": "Hiroto Yamazaki"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Shoichi Ishida"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Aoi Hirano"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Yuri Fukushima"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Masato Sumita"
    },
    {
      "affs": [
        1
      ],
      "name": "Ai Kohata"
    },
    {
      "affs": [
        1,
        6
      ],
      "name": "Kazushi Kinbara"
    },
    {
      "affs": [
        0,
        1,
        2,
        3,
        4,
        5
      ],
      "name": "Kei Terayama"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ehckkr5yjhbev.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15009978/v1",
  "fulltext_url": null,
  "id": "ehckkr5yjhbev",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009978/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",
      "2505|Machine Learning in Materials Science"
    ]
  ],
  "openalex": "W7220687930",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009978/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/ehckkr5yjhbev.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15009978",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009978/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Machine Learning in Materials Science",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Guiding chromatographic purification through data-driven condition recommendations",
  "title_text": "Guiding chromatographic purification through data-driven condition recommendations",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/ehckkr5yjhbev",
  "venue": "ChemRxiv",
  "versions": []
}