{
  "source": "ORD Dataset Wishlist",
  "snapshot_date": "2026-09-25",
  "ord_wishlist_status_at_snapshot": "Available",
  "ord_wishlist_card": "https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126976295",
  "ord_data_issue": null,
  "title": "Evaluating Predictive Accuracy in Asymmetric Catalysis: A Machine Learning Perspective on Local Reaction Space",
  "doi": "10.1021/acscatal.5c01051",
  "journal": "ACS Catalysis",
  "publication_date": "2025-03-31",
  "authors": [
    "Isaiah O. Betinol",
    "Aleksandra Demchenko",
    "Jolene P. Reid"
  ],
  "article_open_access": {
    "status": "closed",
    "license": null
  },
  "dataset_size_estimate": 10000,
  "experiment_type": "literature review",
  "ord_priority": "High",
  "ord_difficulty": "Hard",
  "source_data_kind": "si_spreadsheet_or_csv",
  "smiles_in_source": "partial",
  "needs_author_contact": false,
  "data_locations": [
    {
      "url": "https://doi.org/10.1021/acscatal.5c01051",
      "what": "Article landing page (ACS Catalysis)",
      "license": "closed (OpenAlex); ACS TDM policy per Crossref"
    },
    {
      "url": "https://acs.figshare.com/articles/dataset/Evaluating_Predictive_Accuracy_in_Asymmetric_Catalysis_A_Machine_Learning_Perspective_on_Local_Reaction_Space/28694944",
      "what": "SI zip cs5c01051_si_002.zip (10.8 MB), listed by range request. It holds a code repo whose repo/data/ contains Nature_2019.xlsx, Nature_SMILES.xlsx, Nature_DFT.xlsx, DD_data.xlsx, DD_reactants.xlsx, DD_out-of-bag/Set-1..3.csv, ACS_Catal_2024.xlsx, Chem_Sci_2024.xlsx, JACS_2023.xlsx, Science_2019.xlsx and Angew_2024.csv (24 MB uncompressed). The README says the olefin hydrogenation (Angew 2021) data are not included. LICENSE.txt is MIT (for the software).",
      "license": "CC BY-NC 4.0 (figshare label); MIT LICENSE.txt inside covers the repository software; the data derive from other publications"
    },
    {
      "url": "https://acs.figshare.com/articles/journal_contribution/28694941",
      "what": "SI PDF cs5c01051_si_001.pdf (18.7 MB) describing source references. Listing only.",
      "license": "CC BY-NC 4.0 (figshare label)"
    }
  ],
  "notes": "Literature-derived asymmetric catalysis datasets compiled for ML benchmarking (University of British Columbia), ACS Catalysis 2025, DOI 10.1021/acscatal.5c01051. The SI zip (cs5c01051_si_002.zip on ACS figshare) contains spreadsheets for several case studies taken from earlier publications (files named Nature_2019, Science_2019, DD 2022, ACS_Catal_2024, Chem_Sci_2024, JACS_2023, Angew_2024), several with SMILES. The olefin-hydrogenation (Angew 2021) case study data are not included. Because the reactions come from other papers, each ORD dataset should cite the original publication(s) as well as this compilation. Split by source publication, and check whether any source set is already in ORD before curating it. The spreadsheets likely hold reaction components, catalysts and enantioselectivity (ee or ΔΔG‡); some also hold computed descriptors (e.g. Nature_DFT.xlsx), which should be excluded.",
  "verification_caveats": "Split across roughly 7 source datasets. Outcomes are mainly enantioselectivity (ee or ΔΔG‡), not yields. Literature-compiled entries may lack full conditions. Descriptor columns must be separated from experimental data. The Angew_2024.csv file is large (24 MB). Some source sets may already be in ORD."
}