--- name: retention-time-prediction-optimization description: Use when 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. license: CC-BY-4.0 metadata: edam_operation: http://edamontology.org/operation_3927 edam_topics: - http://edamontology.org/topic_0154 - http://edamontology.org/topic_3372 - http://edamontology.org/topic_3474 tools: - Python - PyG - RDKit - Pandas - torch-scatter - torch-sparse - torch-cluster - NumPy - PyTorch - PyG (PyTorch Geometric) - TorchMetrics - torch-scatter, torch-sparse, torch-cluster techniques: - LC-MS - GC-MS license_tier: restricted derived_from: - doi: 10.1021/acs.jcim.4c02179 title: ABCoRT 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 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 --- # retention-time-prediction-optimization > **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. ## 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 .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."