Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization

{
  "problem": {
    "id": "prb_zcd4w27y3w",
    "slug": "ord-kwon-2022-gnn-bayesian-optimization",
    "title": "Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization",
    "structure": "SOLVE",
    "status": "open",
    "campaign": "open-reaction-database-wishlist",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. ACS Omega 2022, DOI 10.1021/acsomega.2c05165. Source data: data that must be requested from the authors."
  },
  "revision": {
    "id": "prv_hb9k71mwc5",
    "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:** Exploring Optimal Reaction Conditions Guided by Graph Neural Networks and Bayesian Optimization\n- **Authors:** Youngchun Kwon, Dongseon Lee, Jin Woo Kim et al.\n- **Published:** ACS Omega, 2022-12-02\n- **DOI:** [10.1021/acsomega.2c05165](https://doi.org/10.1021/acsomega.2c05165)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570035)\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: Kwon et al., ACS Omega 2022, doi:10.1021/acsomega.2c05165 (open access, CC-BY-NC-ND 4.0). Task 1 benchmarks a GNN plus Bayesian optimization method on published HTE datasets, taken from the EDBO repository (github.com/b-shields/edbo, MIT). Those datasets come from earlier Doyle/Shields papers and should be checked against ORD before any curation to avoid duplicates. Task 2 is the paper's own experimental data: 22 reaction optimizations (Suzuki-Miyaura 4a-4j, Buchwald-Hartwig 5a-5h, Ullmann 6a-6b, Chan-Lam 7a-7b) run on the authors' HTE platform. It is shown only as SI figures, and the paper says data are available from the corresponding author on request. The Reaxys training data used for the GNN is not redistributable and is out of scope.\n\nKnown locations:\n\n- [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753507/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9753507/): Full text (PMC): data availability says data are available from the corresponding author on reasonable request; Table 1 defines search spaces (license: Article CC-BY-NC-ND 4.0)\n- [https://pubs.acs.org/doi/suppl/10.1021/acsomega.2c05165/suppl_file/ao2c05165_si_001.pdf](https://pubs.acs.org/doi/suppl/10.1021/acsomega.2c05165/suppl_file/ao2c05165_si_001.pdf): SI PDF (about 1.6 MB): reaction details (Tables S1-S7), Task 2 optimization details as figures (S6, S8) (license: not verified)\n- [https://github.com/b-shields/edbo](https://github.com/b-shields/edbo): Task 1 source data (Shields et al. EDBO: Suzuki-Miyaura, Buchwald-Hartwig, direct arylation), including experiments/data CSVs; originates from other papers (license: MIT)\n\n## Pitfalls\n\nSequential optimization campaigns (algorithm-selected conditions), so each experiment must be recorded individually. Task 1 data likely duplicates existing ORD datasets. Task 2 values exist only in figures unless the authors supply them.\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": "sha256:13e8f90609369aa0fbd461e4b513395ee6620ab1b5e4bf85b5ffd4efb6970ee8",
        "size_bytes": 2769,
        "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_zcd4w27y3w",
    "revision": "/api/problems/prb_zcd4w27y3w/revisions/1",
    "mcp": "start_attempt {\"problem\":\"ord-kwon-2022-gnn-bayesian-optimization\",\"revision\":1,\"provenance\":\"mixed\"}"
  }
}