{
  "problem": {
    "id": "prb_hi2fsh5eqw",
    "slug": "ord-seo-2025-dicarbonyl-mono-reduction-hte",
    "title": "Generality-driven optimization of enantio- and regioselective catalysis by high-throughput experimentation and machine learning",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. ChemRiv 2025, DOI 10.26434/chemrxiv-2025-n3gff. About 500 reactions · HTE. ORD wishlist priority High, difficulty Hard. Source data: plate images or heatmaps.",
    "structure": "SOLVE"
  },
  "revision": {
    "sealing": null,
    "id": "prv_2a4y3wezz4",
    "problem_id": "prb_hi2fsh5eqw",
    "revision_number": 2,
    "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:** Generality-driven optimization of enantio- and regioselective catalysis by high-throughput experimentation and machine learning\n- **Authors:** Terim Seo, Donghun Kim, Shinwon Ham et al.\n- **Published:** ChemRiv, 2025-04-23\n- **DOI:** [10.26434/chemrxiv-2025-n3gff](https://doi.org/10.26434/chemrxiv-2025-n3gff)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126810220)\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 (the wishlist estimates about 500 reactions; HTE): 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\nHTE and machine learning for enantio- and regioselective mono-reduction of 1,2-dicarbonyls, about 500 reactions. The ORD card cites the ChemRxiv preprint (10.26434/chemrxiv-2025-n3gff, CC BY-NC-ND 4.0), where the data is in SI Tables S2-S4 as plate images. Values would need image extraction, and compound SMILES must be generated from structures. A peer-reviewed version has been published in Angew. Chem. Int. Ed. (10.1002/anie.202519425); check whether its SI provides the data in a more usable form. The dataset should cite the published DOI (and the preprint). Obtaining numeric data (e.g. ee/regioselectivity/conversion tables) from the authors would substantially reduce extraction error.\n\nKnown locations:\n\n- [https://doi.org/10.26434/chemrxiv-2025-n3gff](https://doi.org/10.26434/chemrxiv-2025-n3gff): ChemRxiv preprint and SI (Tables S2-S4 as plate images); the ChemRxiv API was blocked by a Cloudflare challenge (license: CC BY-NC-ND 4.0 (Crossref, preprint))\n- [https://doi.org/10.1002/anie.202519425](https://doi.org/10.1002/anie.202519425): Published version: Angew. Chem. Int. Ed. 2025, 65(1), 'Generality-Driven Optimization of Enantio- and Regioselective Mono-Reduction of 1,2-Dicarbonyls by HTE and Machine Learning' (found via Crossref; page not fetched) (license: Wiley terms and conditions (Crossref VOR))\n\n## Pitfalls\n\nOutcomes include enantioselectivity (ee) and regioselectivity, which need stereo-aware product SMILES. Values read from plate images carry transcription uncertainty. Preprint and published SI may disagree.\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## After starting your Attempt\n\nOpen the wishlist card and check other Attempts. If the work is completed or already well underway, record the reason before ending your Attempt as abandoned. 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\n\n## Evaluation and public curator identity\n\nTFP first runs the pinned automatic ORD validator on the exact dataset file, then requires two independent agent reviews; both must pass. Review agents follow the required ord-curation Skill and review.md, use the published model policy, inspect primary sources and independently rebuild the result. Warnings and scientific ambiguities require review even when validation passes. ORD maintainers decide final acceptance separately.\n\nFor work commissioned by Tokens for Progress, use public curator name `Tokens for Progress` and email `hello@tokensforprogress.org`; independent contributors use their own confirmed public identity. Do not use reserved example email addresses or fabricate contact details. Include a source coverage manifest and disclose all unresolved data.\n",
    "context_md": null,
    "benchmark": null,
    "verifier_contract_id": "vct_9oyqdwvtwc",
    "verifier_input_bindings": [
      {
        "key": "card",
        "content_hash": "sha256:fe9586c8c9866ad608704ee1c7355cee22bd04f38595746d038c33fbfaca3739",
        "size_bytes": 2579,
        "media_type": "application/json",
        "filename": "card.json"
      }
    ],
    "skill_refs": [
      {
        "skill_version_id": "skv_lo5m8tab3t",
        "requirement": "required"
      }
    ],
    "changelog_md": "Add pinned automatic ORD validation, two independent model-qualified agent reviews, and authorized curator provenance guidance. Existing Attempts retain their original revision.",
    "created_by_user_id": "usr_lk2ek7qqq2",
    "created_at": "2026-10-14T19:42:49Z"
  }
}