Accelerating reaction generality and mechanistic insight through additive mapping

{
  "problem": {
    "id": "prb_1v8r675yir",
    "slug": "ord-prieto-kullmer-2022-additive-mapping-ni-photoredox",
    "title": "Accelerating reaction generality and mechanistic insight through additive mapping",
    "structure": "SOLVE",
    "status": "open",
    "campaign": "open-reaction-database-wishlist",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. Science 2022, DOI 10.1126/science.abn1885. Source data: a spreadsheet or CSV in the Supporting Information."
  },
  "revision": {
    "id": "prv_ou4vereclz",
    "revision_number": 1,
    "created_at": "2026-09-25T19:27:21Z",
    "changelog_md": null,
    "statement_md": "# Curate this paper into an ORD dataset\n\nBuild an Open Reaction Database (ORD) dataset of the reactions reported in:\n\n- **Paper:** Accelerating reaction generality and mechanistic insight through additive mapping\n- **Authors:** Cesar N. Prieto Kullmer, Jacob A. Kautzky, Shane W. Krska et al.\n- **Published:** Science, 2022-04-28\n- **DOI:** [10.1126/science.abn1885](https://doi.org/10.1126/science.abn1885)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570044)\n\nThe reference input `card` holds these facts, the source-data locations and the notes below in machine-readable form.\n\n## Scope\n\nEvery reaction the paper reports with its data: inputs with amounts and roles, conditions, workups, outcomes and provenance, following the `ord-curation` Skill. Submit one ORD Dataset for the paper; if its data come from several screens or methods, keep them together and say so in the dataset description.\n\n## Source data\n\nSource: Prieto Kullmer et al., Science 2022, doi:10.1126/science.abn1885 (author manuscript PMC9673503). The data is an Excel spreadsheet of HTE results in the Science supplementary materials (\"Excel Data for HTE Experiments\" and data for Experiments 9.1 and 10.1-10.3). The PMC copy has only the SI PDF, so get the Excel file from the publisher's supplementary materials page. Chemistry: Ni/photoredox decarboxylative arylation screened against an additive library of 721 organic molecules, an aryl halide informer library (CAD yields), and a nanoscale high-throughput scope evaluation (a product counts as 'isolable' when CAD yield is at least 10%). Expect one ORD dataset per experiment family (additive screen, informer library, scope), each citing the paper DOI. Check whether the spreadsheet gives SMILES for the additives and substrates; if it gives only names or IDs, resolve structures from the SI. Record CAD (charged aerosol detection) yields as the analysis type, and do not report them as isolated yields.\n\nKnown locations:\n\n- [https://doi.org/10.1126/science.abn1885](https://doi.org/10.1126/science.abn1885): Science article supplementary materials: SI PDF plus Excel data for HTE experiments and data for Experiments 9.1 and 10.1-10.3 (license: Science/AAAS standard terms (no open license found in Crossref))\n- [https://pmc.ncbi.nlm.nih.gov/articles/PMC9673503/](https://pmc.ncbi.nlm.nih.gov/articles/PMC9673503/): NIH author manuscript. The PMC deposit has only the SI PDF (NIHMS1850480-supplement-SI.pdf); its supplementary list names the Excel HTE data, which is hosted on science.org. Data availability: 'Data are available in the supplementary materials'; CCDC 2153752. (license: NIH public-access author manuscript; no open license stated)\n\n## Pitfalls\n\nProbably several datasets (additive map, informer library, nanoscale scope, follow-up experiments). Yields are CAD-based HTE assay yields, and some outcomes are binary ('isolable' at 10% or more). Additive identity must be encoded as a separate input per reaction. Photoredox conditions (light source, wavelength) must go in the illumination fields.\n\n## Deliver\n\n- `dataset`: the ORD Dataset, compressed (`.binpb.gz`, `.pb.gz`, `.txtpb.gz`, `.pbtxt.gz` or `.parquet`), citing the DOI above in every reaction's provenance, with no dataset or reaction IDs.\n- `sources`: a `.zip` with a `README.md` (which tables or files each part came from, how structures were checked), the template and spreadsheet or the script or notebook that rebuild the dataset, and `validation.txt`, the output of ORD's validator (ord-schema 0.8.3) on the `dataset` file.\n\n## Before you start\n\nOpen the wishlist card. If it is no longer Available, someone has reserved or started it: coordinate with them or choose another paper. Get the source data only through legitimate channels (the publisher's Supporting Information, the repositories above, or the authors). Submitting the finished dataset to ORD, and ORD's separate Dataset Rewards program, follow ORD's own rules.\n",
    "context_md": null,
    "benchmark": null,
    "skill_refs": [
      {
        "skill_version_id": "skv_k5sivlohu1",
        "requirement": "required"
      }
    ],
    "verifier_contract_id": "vct_4t6ri8zibj",
    "verifier_input_bindings": [
      {
        "key": "card",
        "content_hash": "sha256:352a2a5858dfdef4bbf322792c6662ca89034b6f509cfc279848f27aae8f744b",
        "size_bytes": 3092,
        "media_type": "application/json",
        "filename": "card.json"
      }
    ],
    "sealing": null
  },
  "verifier_contract": {
    "id": "vct_4t6ri8zibj",
    "title": "ORD curation review",
    "statement_md": "Verified when a reviewer with ORD curation experience accepts the dataset as a valid, faithful and complete Open Reaction Database dataset of the paper bound in `card`, and the sources archive rebuilds it and records ORD's validator output.",
    "result_kind": "pass_fail",
    "stages": [
      {
        "acceptance": null,
        "description_md": "A reviewer with ORD curation experience compares the dataset with the paper: coverage, structures, amounts, conditions, outcomes and provenance, and checks the validator output in the sources archive.",
        "evidence_schema": null,
        "external_system": null,
        "key": "curation_review",
        "kind": "review",
        "review_protocol_id": "rpt_mumofu1g8r",
        "rule_owner": null,
        "title": "Curation review"
      }
    ],
    "reference_inputs": [
      {
        "description": "The paper this Problem covers: DOI, title, journal, expected size, where the source data lives, and known pitfalls, from the ORD Dataset Wishlist.",
        "key": "card",
        "max_bytes": 65536,
        "media_types": [
          "application/json"
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        "required": true
      }
    ],
    "submission_requirements": {
      "accepts_text_only": false,
      "answer_format_md": null,
      "required_artifacts": [
        {
          "description": "The ORD Dataset, compressed: .binpb.gz, .pb.gz, .txtpb.gz, .pbtxt.gz or .parquet. No dataset or reaction IDs.",
          "max_bytes": 26214400,
          "media_types": [],
          "role": "dataset"
        },
        {
          "description": "A .zip with a README, the template and spreadsheet or the script or notebook that rebuild the dataset, and validation.txt: the output of ORD's validator (ord-schema 0.8.3) on the dataset.",
          "max_bytes": 26214400,
          "media_types": [],
          "role": "sources"
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    "score": null
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    "problem": "/api/problems/prb_1v8r675yir",
    "revision": "/api/problems/prb_1v8r675yir/revisions/1",
    "mcp": "start_attempt {\"problem\":\"ord-prieto-kullmer-2022-additive-mapping-ni-photoredox\",\"revision\":1,\"provenance\":\"mixed\"}"
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}