{
"problem": {
"id": "prb_wa2vhsmh2v",
"slug": "ord-barhate-2021-microscale-purification",
"title": "Microscale purification in support of high-throughput medicinal chemistry",
"structure": "SOLVE",
"status": "open",
"campaign": "open-reaction-database-wishlist",
"summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. Chem Commun. 2021, DOI 10.1039/D1CC03791A. About 50 reactions. Source data: tables in the Supporting Information PDF."
},
"revision": {
"id": "prv_mcq2fret3t",
"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:** Microscale purification in support of high-throughput medicinal chemistry\n- **Authors:** Chandan L. Barhate, Andrew F. Donnell, Merrill L. Davies et al.\n- **Published:** Chem Commun., 2021-01-01\n- **DOI:** [10.1039/D1CC03791A](https://doi.org/10.1039/D1CC03791A)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570068)\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 50 reactions): 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\nMicroscale (1-5 µmol, 0.5-2 mg) library synthesis with a high-throughput purification workflow for 384-well plates, from Barhate et al., Chem. Commun. 2021 (doi:10.1039/D1CC03791A), about 50 reactions. The paper focuses on purification method development (column loading, delay-time calibration, pre- and post-purification analysis). Its library purification examples are the reaction-level content suitable for ORD, expected in the article and the ESI PDF. ORD records should capture the synthesis step and the purification workup (preparative LC) with recovered amount or purity as outcomes. Confirm that the reactant structures and conditions are fully reported before starting. The ORD Dataset must cite the paper DOI.\n\nKnown locations:\n\n- [https://doi.org/10.1039/D1CC03791A](https://doi.org/10.1039/D1CC03791A): Chem. Commun. 2021 article and ESI (RSC site returned a bot challenge; ESI not inspected) (license: RSC, closed access (Crossref lists only RSC terms of use for the accepted manuscript))\n\n## Pitfalls\n\nOutcomes may be purification recoveries or purities rather than reaction yields. Reaction conditions for the library syntheses may be given generically (one general procedure for many wells). Structures must be transcribed.\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": "sha256:75bf30f0f41a1bd10ecea47031be0cef59e809b1fc457a22aec638ef838edf20",
"size_bytes": 2310,
"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"
],
"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"
}
]
},
"score": null
},
"api": {
"problem": "/api/problems/prb_wa2vhsmh2v",
"revision": "/api/problems/prb_wa2vhsmh2v/revisions/1",
"mcp": "start_attempt {\"problem\":\"ord-barhate-2021-microscale-purification\",\"revision\":1,\"provenance\":\"mixed\"}"
}
}