SHREC2025

SHREC 2025: Protein Shape Classification https://shrec2025.drugdesign.fr/

Environment Setup

We recommend using conda to create a clean and reproducible environment.

1. Create Environment from environment.yml

conda env create -f environment.yml
conda activate shrec2025
pip3 install -r requirements.txt

This will install all necessary packages specified in the YAML file.

2. Prepare data

The data can be downloaded by following steps.

curl -o data/train_set_vtk.tar.gz https://shrec2025.drugdesign.fr/files/train_set_vtk.tar.gz
curl -o data/train_set.csv https://shrec2025.drugdesign.fr/files/train_set.csv
curl -o data/test_set_vtk.tar.gz https://shrec2025.drugdesign.fr/files/test_set_vtk.tar.gz
curl -o data/test_set.csv https://shrec2025.drugdesign.fr/files/test_set.csv

mkdir -p data/train_set
tar -xzf data/train_set_vtk.tar.gz -C data/train_set
mkdir -p data/test_set
tar -xzf data/test_set_vtk.tar.gz -C data/test_set

Run the method

1. calculate 3DZD

# for all train_set, do
mkdir -p output/train_set/
python3 vtk2zd.py data/train_set/8ugd_8:R:3U_model1.vtk output/train_set/8ugd_8:R:3U_model1
#
# for all test_set, do
mkdir -p output/test_set/
python3 vtk2zd.py data/test_set/2320.vtk output/test_set/2320

2. calculate volume

python3 calc_volume.py data/train_set.csv output/train_set/ > output/train_set_volume.csv
python3 calc_volume.py data/test_set.csv output/test_set/ > output/test_set_volume.csv

3. run notebook

You can now run make_predictions.ipynb. It will generate test_predictions.csv and test_class_distance_matrix.csv.