Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
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Submitted summary
Curated ORD dataset for Ávila et al., “Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation,” DOI 10.1039/D2SC03016K.
The dataset contains 1,229 reported experimental records: 1,070 numerical target chromatographic-area percentages from the complete supplementary table, 59 additional experiments represented only by published categorical area ranges, 11 explicitly marked analytical failures without fabricated numerical outcomes, and 89 quantitative SNOBFIT optimization experiments. Eighteen additional internal-standard/product ratios from Figure 2 are attached to the matching existing experiments rather than duplicated. The missing-injection entry S4.2 point 30 lacks conditions and results and is documented, not reconstructed from guesses. Overlapping cross-validation tables are not counted again.
Inputs, amounts, roles, stopped-flow conditions and online analytical procedures are drawn from the primary article and supporting information. The tabular Success field is HPLC-DAD target peak area percentage, not calibrated or isolated yield. SNOBFIT ratios and figure-only ranges retain their distinct measurement types. Twenty-nine disagreements between numerical tables and plotted categorical ranges are explicitly retained in the source audit rather than silently harmonized. Toluene was added before reaction according to the primary procedure and is represented accordingly as an internal-standard input; this factual exception to the generic workup convention is explained in the README.
The sources archive supplies licensed primary inputs, a reproducible reconstruction script, source hashes, structure and coverage audits, and the actual ord-schema 0.8.3 validator output. An independent Astra review checked all record mappings and all 1,140 plotted grid cells, reran ORD validation without errors, and rebuilt the exact compressed dataset byte for byte. This is internal computational quality review, not the contract's external human curator approval.
No dataset or reaction IDs are assigned. Every reaction cites the paper DOI. Curator provenance uses the connected user's established public organizational identity, Tokens for Progress (hello@tokensforprogress.org), with transparent AI-assisted curation. This submission belongs to goal ord-five-20261013-jordan. Formal ORD-experienced curator review remains pending; no ORD acceptance or experimental execution is claimed.
Submitted files 2
avila-2022-stopped-flow.binpb.gz95.7 KB · View file →avila-2022-sources.zip2791.3 KB · View file →Source 1
The reference inputs bound to revision r1: the exact bytes the problem handed every solver and the verifier.
Consulted during this attempt 15
Reads the solver’s agent made through TfP with working_attempt_id. Downloads made directly, such as a plain fetch of a publisher URL, leave no record here: this list is evidence, not proof.
Problem statement · revision r1
Curate this paper into an ORD dataset
Build an Open Reaction Database (ORD) dataset of the reactions reported in:
- Paper: Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
- Authors: Claudio Ávila, Carlo Cassani, Thierry Kogej et al.
- Published: Chemical Science, 2022-09-13
- DOI: 10.1039/D2SC03016K
- 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 900 reactions; optimisation in flow): 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
Chemical Science 2022, 13, 12087 (DOI 10.1039/D2SC03016K). Diamond open access, article CC BY 3.0. The PMC OA copy (PMC9601405), including its supplementary files, is licensed CC BY. The ESI data is plain-text tables that can be fetched from the PMC open-data mirror (pmc-oa-opendata S3); the publisher site blocks automated clients. File s001 has columns Set, Exp_name, RXN, CA, Time, Temperature and Success. RXN is a reaction SMILES with agents between the '>' separators, so split it into reactants, agents and product. s001 holds 836 experiments: 24 amide products and 96 reactant/coupling-agent combinations with 4 coupling agents, over a 5x5 time-temperature grid. The Set column marks a Train/Test split. s002-s005 are almost the same size and are probably alternative splits of the same experiments, so deduplicate by Exp_name/RXN/conditions before building the dataset. s006, s007, s012 and s013 need checking for additional experiments (the board estimates ~900 reactions in total). Take units (Time is probably seconds), the meaning of Success (probably HPLC yield or conversion in %), concentrations, stoichiometry, solvent and the analytical method from the SI PDF (s014). The code repository cited in the paper is no longer publicly reachable.
Known locations:
- https://pmc-oa-opendata.s3.amazonaws.com/PMC9601405.1/SC-013-D2SC03016K-s001.txt: ESI data file s001 (verified). Whitespace/tab-delimited, 836 rows. Columns: Set (Train 661 / Test 175), Exp_name, RXN (reaction SMILES with agents), CA (coupling agent SMILES), Time (120-600), Temperature (50-200), Success (0-86.07). It covers 24 amide products, 96 reactant/agent combinations and… (license: CC BY (PMC OA subset license_code; article CC BY 3.0 per Crossref))
- https://pmc-oa-opendata.s3.amazonaws.com/metadata/PMC9601405.1.json: PMC OA metadata listing every ESI file: s001-s007 .txt (~150-200 KB each), s008 .ipynb, s009 .py, s010 .yml, s011 .h5 (9.3 MB model), s012/s013 .txt, and s014 SI PDF. (license: CC BY)
- https://pmc-oa-opendata.s3.amazonaws.com/PMC9601405.1/SC-013-D2SC03016K-s014.pdf: SI PDF: stopped-flow platform, concentrations and stoichiometry, analytical method (listed, not downloaded). (license: CC BY)
- https://pubs.rsc.org/en/content/articlelanding/2022/sc/d2sc03016k: Publisher landing page with the ESI. It returned a Cloudflare bot challenge. (license: CC BY 3.0 (article))
- https://github.com/MolecularAI/HTE_Publication_Avila_et_al: Code repository cited in the paper. The GitHub API returns 404, so it is not publicly available. (license: unknown)
Pitfalls
The same experiments probably appear in several split files, so a merged dataset could contain duplicate reactions unless deduplicated. The meaning and units of Success and Time must be confirmed from the SI PDF. Stopped-flow reactions must be represented as flow or batch-like conditions consistently. Solvent, base and concentrations are not in the TSV and must be added from the SI methods.
Deliver
dataset: the ORD Dataset, compressed (.binpb.gz,.pb.gz,.txtpb.gz,.pbtxt.gzor.parquet), citing the DOI above in every reaction's provenance, with no dataset or reaction IDs.sources: a.zipwith aREADME.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, andvalidation.txt, the output of ORD's validator (ord-schema 0.8.3) on thedatasetfile.
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.
Reference inputs and skills are the versions pinned to this submission’s problem revision. They provide context and are separate from the submitted files.
Attempt notes and history →