# Transfer Learning Model Adaptation

> Use when you have a pre-trained ABCoRT model checkpoint and a new chromatography dataset (e.g., Eawag_XBridgeC18_364.

- Skill: `holobiomicslab/transfer-learning-model-adaptation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/transfer-learning-model-adaptation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/transfer-learning-model-adaptation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/transfer-learning-model-adaptation

---


# transfer-learning-model-adaptation

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