High-Throughput Screening of Reductive Amination Reactions Using Desorption Electrospray Ionization Mass Spectrometry

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  "problem": {
    "id": "prb_lduhj2gui0",
    "slug": "ord-logsdon-2020-desi-ms-reductive-amination",
    "title": "High-Throughput Screening of Reductive Amination Reactions Using Desorption Electrospray Ionization Mass Spectrometry",
    "structure": "SOLVE",
    "status": "open",
    "campaign": "open-reaction-database-wishlist",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. OPRD 2020, DOI 10.1021/acs.oprd.0c00230. Source data: plate images or heatmaps."
  },
  "revision": {
    "id": "prv_tp8gcpd8bs",
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    "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:** High-Throughput Screening of Reductive Amination Reactions Using Desorption Electrospray Ionization Mass Spectrometry\n- **Authors:** David L. Logsdon, Yangjie Li, Tiago J. P. Sobreira et al.\n- **Published:** OPRD, 2020-08-14\n- **DOI:** [10.1021/acs.oprd.0c00230](https://doi.org/10.1021/acs.oprd.0c00230)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126570089)\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\nOrg. Process Res. Dev. 2020, 24, 1647-1657 (DOI 10.1021/acs.oprd.0c00230). A liquid-handling robot combined with desorption electrospray ionization mass spectrometry (DESI-MS) screened 3840 unique reductive amination reactions (amine x carbonyl electrophile x conditions), monitoring products, byproducts and intermediates by full-scan MS, with MS/MS identity checks and LC-MS cross-validation. The article is closed access. Results appear as heat maps in the Supporting Information, and no machine-readable data file is known. A complete dataset would need the numeric DESI-MS data from the authors. Pitfalls: outcomes are MS ion-intensity ratios (semi-quantitative), not yields; reactions may be accelerated in DESI microdroplets or thin films rather than run in bulk solution; and the amine and electrophile structures must be converted to SMILES.\n\nKnown locations:\n\n- [https://pubs.acs.org/doi/10.1021/acs.oprd.0c00230](https://pubs.acs.org/doi/10.1021/acs.oprd.0c00230): Org. Process Res. Dev. 2020, 24, 1647-1657 article and Supporting Information (heat maps per the ORD board notes) (license: ACS publisher copyright (closed access; Crossref lists STM text-and-data-mining policy URLs only))\n\n## Pitfalls\n\nDESI-MS product/reactant ion-ratio outcomes are semi-quantitative, and ORD needs them recorded as relative MS measurements, not yields. Heatmap readings are binned and approximate. Reaction 'conditions' may be droplet or thin-film acceleration on a DESI surface, which is hard to represent. The large combinatorial grid should be generated programmatically once the building-block SMILES are known.\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",
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    "sealing": null
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    "result_kind": "pass_fail",
    "stages": [
      {
        "acceptance": null,
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        "evidence_schema": null,
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    "reference_inputs": [
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        "description": "The paper this Problem covers: DOI, title, journal, expected size, where the source data lives, and known pitfalls, from the ORD Dataset Wishlist.",
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