{
  "problem": {
    "id": "prb_mdj6lcp7q1",
    "slug": "ord-avila-2022-stopped-flow-library-synthesis",
    "title": "Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. Chemical Science 2022, DOI 10.1039/D2SC03016K. About 900 reactions · optimisation in flow. ORD wishlist priority Medium, difficulty Normal. Source data: a spreadsheet or CSV in the Supporting Information.",
    "structure": "SOLVE"
  },
  "revision": {
    "sealing": null,
    "id": "prv_n1ns6v3n48",
    "problem_id": "prb_mdj6lcp7q1",
    "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:** Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation\n- **Authors:** Claudio Ávila, Carlo Cassani, Thierry Kogej et al.\n- **Published:** Chemical Science, 2022-09-13\n- **DOI:** [10.1039/D2SC03016K](https://doi.org/10.1039/D2SC03016K)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570038)\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 900 reactions; optimisation in flow): 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\nChemical Science 2022, 13, 12087 (DOI 10.1039/D2SC03016K). Diamond open access, article CC BY 3.0. The PMC OA copy (PMC9601405), including its supplementary files, is licensed CC BY. The ESI data is plain-text tables that can be fetched from the PMC open-data mirror (pmc-oa-opendata S3); the publisher site blocks automated clients. File s001 has columns Set, Exp_name, RXN, CA, Time, Temperature and Success. RXN is a reaction SMILES with agents between the '>' separators, so split it into reactants, agents and product. s001 holds 836 experiments: 24 amide products and 96 reactant/coupling-agent combinations with 4 coupling agents, over a 5x5 time-temperature grid. The Set column marks a Train/Test split. s002-s005 are almost the same size and are probably alternative splits of the same experiments, so deduplicate by Exp_name/RXN/conditions before building the dataset. s006, s007, s012 and s013 need checking for additional experiments (the board estimates ~900 reactions in total). Take units (Time is probably seconds), the meaning of Success (probably HPLC yield or conversion in %), concentrations, stoichiometry, solvent and the analytical method from the SI PDF (s014). The code repository cited in the paper is no longer publicly reachable.\n\nKnown locations:\n\n- [https://pmc-oa-opendata.s3.amazonaws.com/PMC9601405.1/SC-013-D2SC03016K-s001.txt](https://pmc-oa-opendata.s3.amazonaws.com/PMC9601405.1/SC-013-D2SC03016K-s001.txt): ESI data file s001 (verified). Whitespace/tab-delimited, 836 rows. Columns: Set (Train 661 / Test 175), Exp_name, RXN (reaction SMILES with agents), CA (coupling agent SMILES), Time (120-600), Temperature (50-200), Success (0-86.07). It covers 24 amide products, 96 reactant/agent combinations and… (license: CC BY (PMC OA subset license_code; article CC BY 3.0 per Crossref))\n- [https://pmc-oa-opendata.s3.amazonaws.com/metadata/PMC9601405.1.json](https://pmc-oa-opendata.s3.amazonaws.com/metadata/PMC9601405.1.json): PMC OA metadata listing every ESI file: s001-s007 .txt (~150-200 KB each), s008 .ipynb, s009 .py, s010 .yml, s011 .h5 (9.3 MB model), s012/s013 .txt, and s014 SI PDF. (license: CC BY)\n- [https://pmc-oa-opendata.s3.amazonaws.com/PMC9601405.1/SC-013-D2SC03016K-s014.pdf](https://pmc-oa-opendata.s3.amazonaws.com/PMC9601405.1/SC-013-D2SC03016K-s014.pdf): SI PDF: stopped-flow platform, concentrations and stoichiometry, analytical method (listed, not downloaded). (license: CC BY)\n- [https://pubs.rsc.org/en/content/articlelanding/2022/sc/d2sc03016k](https://pubs.rsc.org/en/content/articlelanding/2022/sc/d2sc03016k): Publisher landing page with the ESI. It returned a Cloudflare bot challenge. (license: CC BY 3.0 (article))\n- [https://github.com/MolecularAI/HTE_Publication_Avila_et_al](https://github.com/MolecularAI/HTE_Publication_Avila_et_al): Code repository cited in the paper. The GitHub API returns 404, so it is not publicly available. (license: unknown)\n\n## Pitfalls\n\nThe same experiments probably appear in several split files, so a merged dataset could contain duplicate reactions unless deduplicated. The meaning and units of Success and Time must be confirmed from the SI PDF. Stopped-flow reactions must be represented as flow or batch-like conditions consistently. Solvent, base and concentrations are not in the TSV and must be added from the SI methods.\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:b5b16ee8e397431495b2addc6604db528d957170ae6adcc590b148bc2d5cac56",
        "size_bytes": 4304,
        "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"
  }
}