{
  "abstract": "C–H borylation has surfaced as a powerful transformation in molecular syntheses, providing direct access to organoboron compounds – highly applicable to pharmaceutical and crop protection industries, as well as materials science. While site-selectivity prediction is established for C(sp 2 )–H borylation, significantly more challenging C(sp 3 )–H borylations remain largely unexplored. In sharp contrast, we have developed a graph-attention deep-learning framework combining molecular representations with quantum-mechanical descriptors to predict relative C(sp 3 )–H borylation site selectivities. Using curated literature reactions and a site labeling algorithm, we evaluated various substrate representations and reaction-condition encodings. Bipartite encoding of reaction conditions outperformed independent component encodings. Validation on previously unreported substrates reproduced experimental selectivity distributions, supporting the framework’s robustness and practical utility.",
  "abstract_shown": true,
  "affiliations": [
    "Institut für Organische und Biomolekulare Chemie, Wöhler Research Institute for Sustainable Chemistry (WISCh)",
    "Georg-August-Universität Göttingen"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Neeraj Kumar Pandit",
      "orcid": "0000-0002-6885-0920"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Max Surke"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Parveen Rawal",
      "orcid": "0000-0002-3268-9020"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Lutz Ackermann",
      "orcid": "0000-0001-7034-8772"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0tqj5581e3qja.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15008230/v1",
  "fulltext_url": null,
  "id": "0tqj5581e3qja",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008230/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": "W7211928545",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15008230/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/0tqj5581e3qja.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.26434/chemrxiv.15008230",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008230/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Graph attention framework for site-selectivity prediction in undirected C(sp3)–H borylation",
  "title_text": "Graph attention framework for site-selectivity prediction in undirected C(sp3)–H borylation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/0tqj5581e3qja",
  "venue": "ChemRxiv",
  "versions": []
}