Retention time prediction optimization

retention-time-prediction-optimization

Summary

Train and optimize graph neural network models to predict liquid chromatography retention times for small molecules using the ABCoRT framework. This skill enables fine-tuning of pre-trained models on domain-specific retention-time datasets via transfer learning, improving prediction accuracy for chemical compound characterization.

When to use

When you have a retention-time dataset (e.g., SMRT or Eawag_XBridgeC18_364) in .xlsx format and need to train or adapt a graph neural network model to predict chromatographic retention times for new compounds. Use this skill when baseline model performance is insufficient and transfer learning from a pre-trained checkpoint can accelerate convergence.

When NOT to use

• Input dataset is not in .xlsx format or lacks expected column structure (SMILES, retention time labels)
• Pre-trained checkpoint is unavailable or corrupted and baseline model training is not feasible
• Retention-time data is from an entirely different chromatographic platform (e.g., GC-MS instead of LC) not covered by pre-training or transfer-learning datasets

Inputs

• Pre-trained model checkpoint (PyTorch .pt or .pth file)
• Retention-time dataset in .xlsx format (e.g., Eawag_XBridgeC18_364.xlsx or SMRT.xlsx)
• SMILES or molecular structure representations (processed via RDKit)
• Command-line arguments specifying dataset path and optional hyperparameters

Outputs

• Fine-tuned model checkpoint saved to disk
• Training logs and metrics (loss, validation accuracy) per epoch
• Inference predictions on test or validation splits
• TorchMetrics monitoring artifacts

How to apply

Execute the training or transfer-learning workflow using Python entry points. For baseline training on the SMRT retention-time dataset, run 'python train_SMRT.py' to instantiate the PyTorch + PyG graph neural network and train from scratch, monitoring loss and validation metrics via TorchMetrics over epochs. For domain adaptation, run 'python train_transfer_FE.py --DataSet <dataset_name>.xlsx' to load a pre-trained checkpoint, fine-tune on your target dataset, and save the adapted model. Monitor training metrics (loss, validation accuracy) to detect convergence; save the final model checkpoint and logs upon completion. The choice between baseline and transfer training depends on data availability: transfer learning is preferred when fine-tuning datasets are small relative to the pre-training corpus.

Related tools

• PyTorch (Deep learning framework for graph neural network model training and optimization)
• PyG (PyTorch Geometric) (Graph neural network library for molecular graph construction and message passing)
• RDKit (Cheminformatics toolkit for converting SMILES to molecular graphs and feature extraction)
• Pandas (Data loading and preprocessing for retention-time .xlsx files)
• TorchMetrics (Real-time training metrics monitoring (loss, validation accuracy) during fine-tuning)
• torch-scatter, torch-sparse, torch-cluster (GPU-accelerated graph operations for efficient PyG tensor aggregation and sampling)

Examples

python train_transfer_FE.py --DataSet Eawag_XBridgeC18_364.xlsx

Evaluation signals

• Training loss decreases monotonically or plateaus at a minimum over epochs, indicating model convergence
• Validation metrics (e.g., mean absolute error on held-out retention times) improve or stabilize, showing generalization
• Saved checkpoint file exists and is loadable by PyTorch without schema errors
• Inference predictions on test set fall within the expected retention-time range (e.g., 1–30 min for typical LC) and match literature or experimental references
• TorchMetrics logs record non-NaN values for all tracked metrics at each epoch

Limitations

• Transfer learning performance depends on similarity between pre-training data (SMRT) and target dataset; significant chromatographic differences may limit transfer gains
• Model assumes molecular structures are representable as graphs with RDKit; SMILES parsing failures will cause training to fail
• No explicit hyperparameter tuning guidance provided in README; optimal learning rate, batch size, and epochs must be determined empirically
• Thirteen transfer-learning datasets are mentioned but only one (Eawag_XBridgeC18_364.xlsx) is exemplified; dataset availability and format consistency are not guaranteed

Evidence

• [readme] train_SMRT.py script entry point for baseline model training: "If you want to train the Model. Please command

python train_SMRT.py
```"
- [readme] transfer learning workflow on thirteen datasets with dataset parameter: "If you want to run the transfer learning on thirteen transfer learning data sets, use:

python train_transfer_FE.py --DataSet Eawag_XBridgeC18_364.xlsx

- [intro] graph neural network architecture training on SMRT retention-time dataset: "Execute the training entry point using Python with the command 'python train_SMRT.py', which will instantiate the graph neural network architecture (PyTorch + PyG)"
- [intro] TorchMetrics monitoring during fine-tuning process: "Monitor training metrics via TorchMetrics during fine-tuning. 4. Save the fine-tuned model checkpoint and training logs."
- [intro] RDKit, PyTorch, and PyG as core tools for molecular representation and training: "Execute train_transfer_FE.py with the --DataSet parameter set to Eawag_XBridgeC18_364.xlsx using PyTorch and PyG for graph neural network operations."