Curate this paper into an ORD dataset

Build an Open Reaction Database (ORD) dataset of the reactions reported in:

• Paper: A direct-to-biology high-throughput chemistry approach to reactive fragment screening
• Authors: Ross P. Thomas, Rachel E. Heap, Francesca Zappacosta et al.
• Published: Chemical Science, 2021-08-11
• DOI: 10.1039/D1SC03551G
• ORD wishlist card: open on GitHub

The reference input card holds these facts, the source-data locations and the notes below in machine-readable form.

Scope

Every reaction the paper reports with its data (the wishlist estimates about 1,000 reactions; HTE, D2B): 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.

Source data

Source: Chem. Sci. 2021, 10.1039/D1SC03551G (CC BY 3.0 article; PMC8457371). This is a direct-to-biology high-throughput chemistry study of about 1000 reactions building reactive fragments. The ESI is a single PDF and does not include the per-reaction data table, so the data must be obtained from the authors, coordinated through ORD. The same data was also used in the PyParse paper (Digital Discovery 2023, 10.1039/D3DD00167A). Check for overlap with any ORD dataset or wishlist item from that paper to avoid duplicates. The PyParse GitHub repository contains example LC-MS data only and has no license.

Known locations:

• https://pmc.ncbi.nlm.nih.gov/articles/PMC8457371/: PMC full text (Europe PMC XML fetched). The only supplementary file is SC-012-D1SC03551G-s001.pdf (ESI). No data-availability statement or repository link. (license: CC-BY-3.0 (article))
• https://doi.org/10.1039/D1SC03551G: Chem. Sci. 2021 article landing page. The RSC page returned 403 to the automated request. (license: CC-BY-3.0 (article))
• https://github.com/thatchemistryguy/PyParse: PyParse LC-MS analysis code repository. It contains example_dataset/ (Waters and Shimadzu raw LC-MS files plus a small platemap CSV). Its relation to this paper's library is unclear, and the repository has no license. (license: none declared)
• https://doi.org/10.1039/D3DD00167A: PyParse paper (Digital Discovery 2023), which reuses data from this study. SI not checked. (license: unknown)

Pitfalls

Direct-to-biology workflow: crude reaction mixtures go straight to biological assays and products are not isolated or characterized. Outcomes are probably LC-MS-based conversion or product detection (semi-quantitative). The data may be split or duplicated with the PyParse dataset. Library-scale enumeration needs templated SMILES.

Deliver

• 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.
• 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.

Before you start

Open 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.