Graph Neural Network Model Training

---
name: graph-neural-network-model-training
description: Use when when you have a molecular dataset (e.g., SMRT retention-time
  pairs) that you want to model as node-and-edge graphs, a PyTorch + PyG architecture
  already instantiated, and need to perform supervised training with checkpoint persistence
  and per-epoch metric logging.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3445
  edam_topics:
  - http://edamontology.org/topic_0154
  - http://edamontology.org/topic_3474
  - http://edamontology.org/topic_0092
  tools:
  - Python
  - PyG
  - RDKit
  - NumPy
  - Pandas
  - torch-scatter
  - torch-sparse
  - torch-cluster
  - PyTorch
  - PyG (PyTorch Geometric)
  - TorchMetrics
  - torch-scatter, torch-sparse, torch-cluster
  license_tier: restricted
  provenance_tier: literature
derived_from:
- doi: 10.1021/acs.jcim.4c02179
  title: ABCoRT
evidence_spans:
- '**Python**'
- '**PyG**'
- '**RDKit**'
- '- **RDKit**'
- '**NumPy**'
- '**Pandas**'
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_abcort_cq
    doi: 10.1021/acs.jcim.4c02179
    title: ABCoRT
  dedup_kept_from: coll_abcort_cq
schema_version: 0.2.0
attribution:
  generator: AgenticScienceBuilder
  original_doi: 10.1021/acs.jcim.4c02179
  all_source_dois:
  - 10.1021/acs.jcim.4c02179
  zenodo_doi: 10.5281/zenodo.20794027
  curators: []
  promoter: Louis-Félix Nothias
  sponsor: CNRS & Université Côte d'Azur
---

# graph-neural-network-model-training

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

Train a PyTorch-based graph neural network (GNN) on molecular retention-time prediction data using PyG primitives and TorchMetrics monitoring. This skill applies to projects that model molecular properties as graph-structured data and require supervised training with epoch-level loss and validation tracking.

## When to use

When you have a molecular dataset (e.g., SMRT retention-time pairs) that you want to model as node-and-edge graphs, a PyTorch + PyG architecture already instantiated, and need to perform supervised training with checkpoint persistence and per-epoch metric logging.

## When NOT to use

- Input dataset is not graph-structured or lacks molecular property annotations.
- Model architecture is not yet defined in PyTorch + PyG; use model design/prototyping skill first.
- You need transfer learning evaluation; use graph-neural-network-transfer-learning skill after training completes.

## Inputs

- SMRT retention-time dataset (formatted to match train_SMRT.py input schema)
- PyTorch model architecture definition
- Training hyperparameter configuration (learning rate, batch size, epochs)

## Outputs

- Trained model checkpoint (saved to disk)
- Training loss curve (per-epoch TorchMetrics logs)
- Validation metrics (loss, accuracy, or domain-specific scores per epoch)

## How to apply

Execute the training entry point (python train_SMRT.py) from the repository root after verifying the train_SMRT.py script is present and the input dataset is in the format expected by the script. The training workflow instantiates a graph neural network architecture using PyTorch and PyG, feeds the SMRT retention-time dataset through the graph model, and optimizes via backpropagation over multiple epochs. Monitor training progress via TorchMetrics, which logs loss and validation metrics at each epoch. Upon convergence or when a stopping criterion is met, save the trained model checkpoint to disk for downstream transfer learning or inference tasks.

## Related tools

- **PyTorch** (Core deep-learning framework for model instantiation, forward pass, and backpropagation during training.)
- **PyG (PyTorch Geometric)** (Graph neural network library; provides graph data structures, message-passing layers, and GNN primitives used in the model architecture.)
- **TorchMetrics** (Tracks and logs loss and validation metrics across training epochs for monitoring convergence and generalization.)
- **RDKit** (Molecular toolkit for preprocessing and featurizing the chemical compounds in the retention-time dataset.)
- **torch-scatter, torch-sparse, torch-cluster** (PyG dependencies that enable efficient aggregation and pooling operations over graph nodes and edges.)

## Examples

```
python train_SMRT.py
```

## Evaluation signals

- Training loss decreases monotonically or asymptotically over epochs, indicating model learning.
- Validation loss follows training loss without large divergence, suggesting absence of severe overfitting.
- Model checkpoint file is created and can be loaded back without errors (e.g., via torch.load).
- TorchMetrics output logs contain epoch-wise entries for all tracked metrics (loss, accuracy, or domain-specific scores).
- Trained model produces predictions on held-out test data with reasonable error bounds relative to ground truth retention times.

## Limitations

- Training on SMRT dataset only; dataset-specific hyperparameters may not generalize to other retention-time or molecular-property prediction tasks.
- No changelog provided in the repository; reproducibility across versions may be compromised if dependencies are updated.
- Script assumes the train_SMRT.py file contains all necessary preprocessing and data-loading logic; incorrect dataset format will cause silent failures or cryptic PyTorch errors.

## Evidence

- [other] The ABCoRT model is trained on the SMRT retention-time dataset by executing the command 'python train_SMRT.py'.: "execute the training entry point using Python with the command 'python train_SMRT.py', which will instantiate the graph neural network architecture (PyTorch + PyG) and train on the SMRT data"
- [other] Training is monitored via TorchMetrics for loss and validation metrics over epochs, and the model is saved after convergence.: "Monitor training via TorchMetrics for loss and validation metrics over epochs. 5. Save the trained model checkpoint to disk upon convergence or completion."
- [readme] The training script is invoked as a Python entry point after dataset preparation.: "If you want to train the Model. Please command 
```
python train_SMRT.py
```"
- [readme] PyTorch, PyG, and related dependencies are core to the training infrastructure.: "**Pytorch**, **PyG**, **torch-scatter**, **torch-sparse**, **torch-cluster**, **torch_geometric**"