| ---
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| license: other
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| library_name: pytorch
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| tags:
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| - chromatography
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| - retention-time
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| - graph-neural-network
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| - reproducibility
|
| ---
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|
|
| # Leakage-controlled multi-laboratory RT revision release
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|
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| This frozen release corresponds to the revised validation of the manuscript.
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| It contains GAT, GCN, and FPNN checkpoints for six inner folds in each of three
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| identity-grouped and three scaffold-aware outer repetitions; fitted stack
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| models; preprocessing objects; the controlled FPNN one-hot checkpoints;
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| configuration and analysis code; aggregate metrics; task-matched bootstrap
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| outputs; hashed split assignments; and de-identified model predictions.
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|
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| The previous top-level seven-checkpoint FPNN bundle has been superseded. The
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| scientific analysis uses exactly six inner folds per outer repetition.
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|
|
| ## Validation scope
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|
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| - Identity-grouped stack: R2 0.977 +/- 0.002, MAE 0.360 +/- 0.016 min,
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| RMSE 0.578 +/- 0.057 min.
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| - Scaffold-aware stack: R2 0.928 +/- 0.004, MAE 0.670 +/- 0.013 min,
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| RMSE 0.911 +/- 0.022 min.
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| - Scope: new molecular identity groups or scaffold components assigned to one
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| of the 23 laboratory labels represented during training. No unseen-method
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| claim is made.
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|
|
| ## Descriptor routing
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|
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| GAT and GCN each use a five-descriptor branch. FPNN uses a Morgan fingerprint
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| and laboratory encoding but no molecular descriptors. The complete ExtraTrees
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| stack receives three base predictions plus the five descriptors. The public
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| ablation labelled `base_predictions_only` removes the five direct meta-learner
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| descriptor columns; it is not a descriptor-free retraining of GAT or GCN.
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|
|
| ## Reproduction
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|
|
| Create the recorded environment and run:
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|
|
| ```bash
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| pip install -r requirements.txt
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| python reproduce.py --data /path/to/authorized/train.csv --output /new/empty/output --include-neural
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| ```
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|
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| The input must contain `SMILES`, `Lab`, and `RT` columns and must be obtained
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| through an authorized HighResNPS-derived data-access route. Training is the
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| long stage. Aggregate frozen outputs used for the paper are already included
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| under `artifacts/summary`.
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|
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| ## Public/private data boundary
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|
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| HighResNPS states that the service is for a closed user group, and recent
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| published uses describe license restrictions on public redistribution of its
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| metadata and spectra. This repository therefore does not redistribute SMILES,
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| experimental RT values, laboratory identifiers, or raw source rows. Each split
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| file instead uses a SHA-256 key computed from
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| `record_index + SMILES + Lab + RT`; an authorized user with the same modelling
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| table can reconstruct the key and verify every assignment. Public prediction
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| files contain that key and model outputs only, without source fields or targets.
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|
|
| ## Directory map
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|
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| - `src/`: model and feature code used by the frozen pipeline.
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| - `revision/config/`: frozen analysis configuration.
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| - `revision/scripts/`: split, training, summarization, figure/table source code.
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| - `artifacts/{identity_grouped,scaffold_aware}/repeat_{1,2,3}/`: six-fold
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| checkpoints, fitted stacks, preprocessing, metrics, hashed splits, and
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| de-identified predictions.
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| - `artifacts/matched_lab_encoding/`: architecture-matched one-hot FPNN
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| checkpoints and aggregate comparison.
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| - `artifacts/summary/`: aggregate metrics, corrected paired bootstrap results,
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| and figure/table source summaries.
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| - `SHA256SUMS.txt`: checksum manifest for the complete release.
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|
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| No DOI is claimed. The repository commit and release tag identify the frozen
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| public version.
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|
