Chromatographic Method Transfer Learning

---
name: chromatographic-method-transfer-learning
description: 'Use when you have experimental RT measurements from a source chromatographic
  method and need to predict RTs for the same molecules on a target chromatographic
  method, but lack a large calibration dataset (typical scenario: 10–100 molecules
  with ground truth RTs on both methods).'
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3435
  edam_topics:
  - http://edamontology.org/topic_0602
  - http://edamontology.org/topic_3520
  - http://edamontology.org/topic_3473
  tools:
  - alvaDesc
  - cmmrt (CMM-RT)
  techniques:
  - LC-MS
  license_tier: restricted
  provenance_tier: literature
derived_from:
- doi: 10.1186/s13321-022-00613-8
  title: cmmrt
evidence_spans:
- 5,666 molecular descriptors and 2,214 fingerprints (MACCS166, Extended Connectivity,
  and Path Fingerprints fingerprints) were generated with the alvaDesc software
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_cmmrt_cq
    doi: 10.1186/s13321-022-00613-8
    title: cmmrt
  dedup_kept_from: coll_cmmrt_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1186/s13321-022-00613-8
  all_source_dois:
  - 10.1186/s13321-022-00613-8
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# chromatographic-method-transfer-learning

> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->
## Summary

Apply Bayesian meta-learning to project retention times (RTs) across chromatographic methods using a minimal calibration set (≥10 molecules), enabling rapid method transfer without full retraining. This skill bridges different LC-MS platforms by learning a probabilistic RT projection mapping from a small set of known compounds.

## When to use

You have experimental RT measurements from a source chromatographic method and need to predict RTs for the same molecules on a target chromatographic method, but lack a large calibration dataset (typical scenario: 10–100 molecules with ground truth RTs on both methods). Use this skill when complete retraining on the target method is impractical or when you need to integrate a new instrument into an existing metabolomics workflow.

## When NOT to use

- Calibration dataset is already large (>500 molecules): standard supervised retraining on the target method will be more stable and accurate than meta-learning.
- Source and target methods are fundamentally incompatible (e.g., completely different separation chemistry or detector type): RT projections may not preserve ranking or linearity.
- Only a single molecule is available for calibration: meta-learning requires ≥10 molecules to learn a meaningful prior.

## Inputs

- Calibration molecule set with SMILES or chemical structure files (SDF, mol, mol2, hin)
- Experimental retention times for calibration molecules on both source and target chromatographic methods
- Molecular descriptor/fingerprint vectors (MACCS166, Extended Connectivity, Path Fingerprints) generated by alvaDesc
- Test molecule set with known RTs on source method (to be projected to target method)

## Outputs

- Trained Bayesian meta-learned Gaussian Process prior (saved model object)
- Projected retention times for test molecules on the target chromatographic method
- Mean absolute error (MAE) and median absolute error (MedAE) metrics comparing projected vs. experimental RTs
- Performance comparison table against baseline methods (linear regression, standard regressors)

## How to apply

First, generate molecular descriptors and fingerprints (MACCS166, Extended Connectivity, Path Fingerprints) for all calibration molecules using alvaDesc software. Train a Bayesian meta-learning Gaussian Process on this small set (≥10 molecules) to learn the RT projection mapping between source and target chromatographic methods; the meta-learner infers a prior from the calibration data rather than starting from scratch. Apply the trained meta-learner to project RTs for test molecules onto the target method. Evaluate performance by calculating mean absolute error (MAE) and median absolute error (MedAE) of projected RTs against ground-truth experimental values on held-out test molecules. Compare error rates (target: ≤40 s MAE, ≤20 s MedAE based on the DNN baseline) against traditional linear regression or standard ML regressors to confirm the meta-learned projection is competitive.

## Related tools

- **alvaDesc** (Generates 5,666 molecular descriptors and 2,214 fingerprints (MACCS166, Extended Connectivity, Path Fingerprints) required as input features for the meta-learning model) — https://www.alvascience.com/alvadesc/
- **cmmrt (CMM-RT)** (Reference implementation of Bayesian meta-learning for RT projection; provides notebooks, data processing pipelines, and trained models for fingerprint generation and GP meta-training) — https://github.com/constantino-garcia/cmmrt

## Examples

```
python cmmrt/rt/train_model.py --storage sqlite:///results/optuna/train.db --save_to saved_models; python -c "from cmmrt.projections import meta_train_gp; gp = meta_train_gp(calibration_fingerprints, calibration_rts_source, calibration_rts_target, n_epochs=100); projected_rts = gp.predict(test_fingerprints)"
```

## Evaluation signals

- MAE and MedAE of projected RTs should be ≤40 s and ≤20 s respectively (competitive with DNN baseline 39.2±1.2 s and 17.2±0.9 s).
- Projection error should not degrade substantially when calibration set size is exactly 10 molecules (paper's minimum threshold).
- Performance on test molecules should not be significantly worse than baselines (linear regression, standard ML regressors) measured on the same held-out test set.
- Predicted RT ranks for test molecules should preserve compound ordering relative to the target chromatographic method (ordinal correlation).
- Meta-trained model should generalize across different target chromatographic methods without retraining (transfer learning invariant).

## Limitations

- Requires alvaDesc software (commercial license) to generate fingerprints; no open-source fingerprint generation substitute is mentioned in the repository.
- Performance depends critically on calibration molecule representativeness: if calibration set is biased toward short or long retention times, projection accuracy may degrade for underrepresented chemical space.
- Method assumes RT correlation is learnable from a small calibration set; highly non-linear or noisy RT relationships between methods may exceed meta-learning's capacity.
- Code repository is marked as under active development; reproducibility depends on using the `paper` branch as noted in the README, not the main development branch.

## Evidence

- [intro] A novel Bayesian meta-learning approach is proposed for RT projection between CMs from as few as 10 molecules while still obtaining competitive error rates compared with previous approaches.: "A novel Bayesian meta-learning approach is proposed for RT projection between CMs from as few as 10 molecules while still obtaining competitive error rates compared with previous approaches."
- [intro] 5,666 molecular descriptors and 2,214 fingerprints (MACCS166, Extended Connectivity, and Path Fingerprints fingerprints) were generated with the alvaDesc software: "5,666 molecular descriptors and 2,214 fingerprints (MACCS166, Extended Connectivity, and Path Fingerprints fingerprints) were generated with the alvaDesc software"
- [intro] The best results were obtained by a heavily regularized DNN trained with cosine annealing warm restarts and stochastic weight averaging, achieving a mean and median absolute errors of 39.2±1.2 s and 17.2 ± 0.9 s, respectively.: "The best results were obtained by a heavily regularized DNN trained with cosine annealing warm restarts and stochastic weight averaging, achieving a mean and median absolute errors of 39.2±1.2 s and"
- [readme] To train your own model or to predict the RT of your own set of compounds it is necessary to generate the fingerprints using alvaDesc software (under license, check alvadesc software).: "To train your own model or to predict the RT of your own set of compounds it is necessary to generate the fingerprints using alvaDesc software (under license, check alvadesc software)."
- [readme] Note that, to integrate the proposal into the CEU Mass Mediator platform, the code in this repository will continue to be developed. Hence, branch `paper` should be used as reference for reproducing the results of the paper.: "Note that, to integrate the proposal into the CEU Mass Mediator platform, the code in this repository will continue to be developed. Hence, branch `paper` should be used as reference for reproducing"