{
  "abstract": "As idealized structural fragments for guiding bond disconnections, synthons are considered the conceptual backbone of retrosynthetic analysis. Yet retrosynthesis can be difficult because the choice of synthons requires extensive experience in organic synthesis, which prevents non-experts from accessing desired products. Here we address this knowledge gap by constructing a curated, machine interpretable database of commercially available synthon equivalents (hereafter valid synthons) distilled from large-scale retrosynthetic analysis. Mining over 40 million molecules from PubChem, we identify 1,617 essential valid synthons. These synthons, organized into (i) molecular backbones, (ii) functional groups, and (iii) linker motifs, were obtained via iterative retrosynthetic decomposition and stringent frequency based filtering tied to documented reaction precedents. The resulting set enables the in silico reconstruction of over 94% of these 40 million molecules. We demonstrate that such synthons reduce dependence on expansive reagent inventories, even though the routes themselves are non-conventional and algorithm-generated: using only seven representative valid synthons, we accessed three structurally and functionally divergent targets (melatonin, a natural sleep hormone; an azo dye; and a photoactive melatonin derivative), illustrating the breadth and practicality of a synthons to products methodology in the context of automated synthesis for non-experts in organic synthesis.",
  "abstract_shown": true,
  "affiliations": [
    "Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China",
    "State Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Baicheng Zhang",
      "orcid": "0000-0002-1899-028X"
    },
    {
      "affs": [
        0
      ],
      "name": "Jing Chen"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Xiaolong Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Pieter E. S. Smith"
    },
    {
      "affs": [
        0
      ],
      "name": "Aoyuan Cheng"
    },
    {
      "affs": [
        1
      ],
      "name": "Runyang Miao"
    },
    {
      "affs": [
        0
      ],
      "name": "Hongping Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Linjiang Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Bin Jiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Guoqing Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Yi Luo"
    },
    {
      "affs": [
        1
      ],
      "name": "Jun Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/71krb3gfhm49d.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008417/v1",
  "fulltext_url": null,
  "id": "71krb3gfhm49d",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008417/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": "W7211882613",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008417/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-07",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/71krb3gfhm49d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.26434/chemrxiv.15008417",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008417/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Near-Complete Synthon Library via Retrosynthetic Analysis for Automated Organic Synthesis",
  "title_text": "Near-Complete Synthon Library via Retrosynthetic Analysis for Automated Organic Synthesis",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:17Z",
  "url": "https://research.draconitic.com/paper/71krb3gfhm49d",
  "venue": "ChemRxiv",
  "versions": []
}