Enabling synthesis in fragment-based drug discovery (FBDD): microscale high-throughput optimisation of the medicinal chemist's toolbox reactions

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  "problem": {
    "id": "prb_vc2zhri7cr",
    "slug": "ord-townley-2023-fbdd-microscale-hte",
    "title": "Enabling synthesis in fragment-based drug discovery (FBDD): microscale high-throughput optimisation of the medicinal chemist's toolbox reactions",
    "structure": "SOLVE",
    "status": "open",
    "campaign": "open-reaction-database-wishlist",
    "summary": "Curate this paper's reaction data into a validated Open Reaction Database dataset. RSC Med Chem 2023, DOI 10.1039/D3MD00495C. About 800 reactions · HTE. Source data: plate images or heatmaps."
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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:** Enabling synthesis in fragment-based drug discovery (FBDD): microscale high-throughput optimisation of the medicinal chemist's toolbox reactions\n- **Authors:** Chloe Townley, Davide Branduardi, Gianni Chessari et al.\n- **Published:** RSC Med Chem, 2023-01-01\n- **DOI:** [10.1039/D3MD00495C](https://doi.org/10.1039/D3MD00495C)\n- **ORD wishlist card:** [open on GitHub](https://github.com/orgs/open-reaction-database/projects/11?pane=issue&itemId=126569960)\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 800 reactions; HTE): 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\nPaper: Townley et al., RSC Med. Chem. 2023, doi:10.1039/D3MD00495C (free to read in PMC, PMC10718589; RSC terms, not CC-licensed). It uses pre-made 96-well fractional-factorial screening kits for Suzuki-Miyaura (SMC), Buchwald-Hartwig (BHC) and SNAr reactions on fragment-like, heteroatom-rich substrates such as bromo-7-azaindole. The ORD board estimates about 800 reactions. Plates were analysed by MISER flow-injection single-ion monitoring and shown as heatmaps. Top hits were re-analysed by UHPLC-MS (LCAP) and scaled to 0.25 mmol with isolated yields. The single ESI PDF may hold the screen layouts and follow-up data. Check whether it gives per-well numeric values; if not, heatmap values require author-supplied data. Screen conditions (catalyst, ligand, base, solvent per well) come from the kit designs. MISER responses are relative ion counts, not yields.\n\nKnown locations:\n\n- [https://pmc.ncbi.nlm.nih.gov/articles/PMC10718589/](https://pmc.ncbi.nlm.nih.gov/articles/PMC10718589/): PMC full text of RSC Med. Chem. 2023 article; lists a single supplementary file MD-014-D3MD00495C-s001.pdf (4.4 MB) (license: RSC terms (PMC free-to-read copy; not in the PMC open-access subset; no CC license found))\n- [https://pmc.ncbi.nlm.nih.gov/articles/instance/10718589/bin/MD-014-D3MD00495C-s001.pdf](https://pmc.ncbi.nlm.nih.gov/articles/instance/10718589/bin/MD-014-D3MD00495C-s001.pdf): ESI PDF: workflow, screening-kit designs, follow-up UHPLC-MS data and compiled historical reaction data (license: RSC terms)\n- [https://doi.org/10.1039/D3MD00495C](https://doi.org/10.1039/D3MD00495C): RSC publisher landing page and ESI (license: RSC terms)\n\n## Pitfalls\n\nMISER heatmap responses are relative single-ion MS signals, not yields or conversions, so they must be encoded as the right measurement type (for example AREA or COUNTS) and not as yield. There are three reaction classes (SMC, BHC, SNAr), so templates differ across plates. The follow-up LCAP and isolated yields are a small subset. Historical ELN-derived comparison data in the ESI may be aggregate statistics rather than individual reactions.\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,
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      {
        "skill_version_id": "skv_k5sivlohu1",
        "requirement": "required"
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    "verifier_contract_id": "vct_4t6ri8zibj",
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    "result_kind": "pass_fail",
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        "evidence_schema": null,
        "external_system": null,
        "key": "curation_review",
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        "review_protocol_id": "rpt_mumofu1g8r",
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        "title": "Curation review"
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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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