dpa_adapt

ADAPT example

This directory contains a small ready-to-run example for dpa_adapt. The example uses 8 pre-processed QM9 molecules to fine-tune and evaluate a DPA-based HOMO-LUMO gap predictor.

The processed data is already included, so you can run the demo directly.

Directory layout

examples/dpa_adapt/
|-- data/                         # ready-to-use processed data
|   |-- train/                    # 5 training systems in deepmd/npy format
|   |-- test/                     # 3 test systems in deepmd/npy format
|   |-- train_labels.npy
|   `-- test_labels.npy
|-- scripts/
|   |-- run_evaluate_frozen_sklearn.py   # frozen_sklearn demo: DPA-3.1-3M + Ridge
|   |-- run_evaluate_frozen_head.py      # frozen_head demo: DPA-3.1-3M fine-tuning
|   `-- prepare_data.py           # regenerate data/ from raw GDB9 data
`-- README.md

Run the example

Two evaluation scripts are provided, demonstrating different adaptation strategies.

From this directory, run either (or both):

# frozen_sklearn strategy - extract DPA features, fit a Ridge regressor
python scripts/run_evaluate_frozen_sklearn.py

# frozen_head strategy - fine-tune the prediction head with gradient steps
python scripts/run_evaluate_frozen_head.py

If you do not activate the virtual environment, run the same commands with the environment's Python executable, for example:

../../../.venv/Scripts/python.exe scripts/run_evaluate_frozen_head.py

run_evaluate_frozen_sklearn.py

Uses the frozen_sklearn strategy with the Domains_Drug model branch. DPA-3.1-3M features are extracted from the training systems and a Ridge (linear) regressor is fitted on top. Prints MAE, RMSE, and R2 on the test set.

run_evaluate_frozen_head.py

Uses the frozen_head strategy. A fresh prediction head is trained on top of frozen DPA-3.1-3M features with learning_rate=1e-3, batch_size=128, max_steps=5. Prints predictions and evaluation metrics (MAE, RMSE, R2) on the test set.

About the included data

The data/ directory already contains the processed example dataset. Each system is stored in deepmd/npy format. The included split has 5 training systems and 3 test systems. Each set.000/ directory contains a gap.npy label file. The label key used by the example is gap.

In normal use, you do not need to run any data preparation step.

Regenerating the data

scripts/prepare_data.py is provided only for reproducibility. It rebuilds the included data/ directory from raw GDB9/QM9 files.

Run it only if you want to recreate the processed data:

python scripts/prepare_data.py

The script downloads gdb9.tar.gz, extracts the raw SDF and CSV files into raw/, converts the first 8 molecules to deepmd/npy, and writes HOMO-LUMO gap labels as gap.npy.