--- name: transfer-learning-model-adaptation description: Use when you have a pre-trained ABCoRT model checkpoint and a new chromatography dataset (e.g., Eawag_XBridgeC18_364. license: CC-BY-4.0 metadata: edam_operation: http://edamontology.org/operation_3445 edam_topics: - http://edamontology.org/topic_0154 - http://edamontology.org/topic_3407 - http://edamontology.org/topic_0091 - http://edamontology.org/topic_3375 tools: - Python - PyG - RDKit - Pandas - torch-scatter - torch-sparse - torch-cluster - train_transfer_FE.py - PyTorch - PyG (PyTorch Geometric) - TorchMetrics - torch-scatter, torch-sparse, torch-cluster - torch - scikit-learn - tqdm - rdkit-pypi - torch_geometric - RT-Transformer license_tier: restricted derived_from: - doi: 10.1021/acs.jcim.4c02179 title: ABCoRT - doi: 10.1093/bioinformatics/btae084 title: '' - doi: 10.1038/s41467-019-13680-7 title: '' evidence_spans: - '**Python**' - '**PyG**' - '**RDKit**' - '- **RDKit**' - '**Pandas**' - '**torch-scatter**' 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 - build: coll_rt_transformer_cq doi: 10.1093/bioinformatics/btae084 title: RT-Transformer 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 - 10.1093/bioinformatics/btae084 - 10.1038/s41467-019-13680-7 zenodo_doi: 10.5281/zenodo.20794027 curators: [] promoter: Louis-Félix Nothias sponsor: CNRS & Université Côte d'Azur --- # transfer-learning-model-adaptation > **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. ## Summary Fine-tune a pre-trained graph neural network model on a domain-specific chromatography dataset using PyTorch and PyG to adapt learned representations for improved retention time prediction on new chemical libraries. This skill enables rapid model customization without retraining from scratch. ## When to use You have a pre-trained ABCoRT model checkpoint and a new chromatography dataset (e.g., Eawag_XBridgeC18_364.xlsx with SMILES and measured retention times) from a different column, instrument, or chemical space, and you need to adapt the model's feature extraction layers to this domain without full retraining. ## When NOT to use - The input dataset is not in .xlsx format or lacks SMILES and retention time columns; preprocess to this schema first. - You are training a model from scratch (no pre-trained checkpoint available); use the main training script (train_SMRT.py) instead. - The dataset is from the same chromatography platform and chemical space as the original training data; direct inference without fine-tuning may be sufficient. ## Inputs - Pre-trained ABCoRT model checkpoint (.pth or similar PyTorch format) - Transfer-learning dataset file (.xlsx format with SMILES column and retention time measurements) - Command-line parameters (--DataSet flag with dataset filename) ## Outputs - Fine-tuned model checkpoint with adapted feature extraction layers - Training logs and metrics (loss, validation accuracy, monitored via TorchMetrics) - Retention time predictions on the new chromatography domain ## How to apply Load the pre-trained model checkpoint and the transfer-learning dataset (supplied as .xlsx with SMILES and retention time columns) into memory using Pandas and Python. Execute the train_transfer_FE.py script, passing the dataset filename via the --DataSet command-line flag; the script will convert SMILES to molecular graphs using RDKit, construct feature embeddings via PyG, and perform fine-tuning on the feature extraction layers using PyTorch. Monitor training loss and validation metrics via TorchMetrics throughout the epochs to detect convergence and overfitting. Save the resulting fine-tuned checkpoint and training logs for subsequent inference on new compounds. ## Related tools - **train_transfer_FE.py** (Main script that orchestrates fine-tuning of the feature extraction layers using the transfer dataset) — github.com/RiverCCC/ABCoRT - **PyTorch** (Deep learning framework for model loading, parameter optimization, and checkpoint management) - **PyG (PyTorch Geometric)** (Graph neural network library for constructing molecular graphs and graph convolution operations) - **RDKit** (Cheminformatics toolkit for converting SMILES strings to molecular graph representations) - **Pandas** (Data loading and preprocessing of the .xlsx transfer dataset) - **TorchMetrics** (Monitoring training and validation metrics during fine-tuning to assess convergence) - **torch-scatter, torch-sparse, torch-cluster** (PyG dependencies providing efficient graph operations and aggregation) ## Examples ``` python train_transfer_FE.py --DataSet Eawag_XBridgeC18_364.xlsx ``` ## Evaluation signals - Training loss converges smoothly over epochs and validation loss plateaus without divergence, as monitored via TorchMetrics. - Fine-tuned model predictions on a held-out test subset of the transfer dataset exhibit lower mean absolute error (MAE) or root mean squared error (RMSE) in retention time compared to the base pre-trained model, confirming domain adaptation. - The saved fine-tuned checkpoint loads successfully in PyTorch and produces predictions with the same input/output shape and dimensionality as the original model. - Training logs record all hyperparameters (learning rate, batch size, number of epochs) and final metric values for reproducibility. - The fine-tuned model generalizes to new compounds in the same chromatography domain without catastrophic forgetting of pre-trained representations. ## Limitations - Transfer learning performance depends on the size and representativeness of the target dataset; very small datasets (< 50 compounds) may overfit. - The approach assumes the new dataset uses the same molecular representation (SMILES) and retention time measurement protocol as the pre-training data; incompatible formats will require preprocessing. - No changelog or version history is documented in the repository, making it difficult to track which pre-trained checkpoint versions are compatible with specific transfer datasets. ## Evidence - [other] Transfer learning on the Eawag_XBridgeC18_364 dataset is executed by running the train_transfer_FE.py script with the dataset name passed as a command-line argument via the --DataSet flag.: "Execute train_transfer_FE.py with the --DataSet parameter set to Eawag_XBridgeC18_364.xlsx" - [other] The workflow involves loading pre-trained checkpoint, executing the transfer learning script with PyTorch and PyG, monitoring via TorchMetrics, and saving the fine-tuned model.: "Load the pre-trained model checkpoint and transfer-learning dataset (Eawag_XBridgeC18_364.xlsx) using Python and Pandas. 2. Execute train_transfer_FE.py with the --DataSet parameter set to" - [intro] The project involves training a model and running transfer learning on thirteen transfer learning datasets.: "train the Model and run the transfer learning on thirteen transfer learning data sets" - [readme] Repository README documents the exact CLI invocation for transfer learning.: "python train_transfer_FE.py --DataSet Eawag_XBridgeC18_364.xlsx"