We highly recommend installing Anaconda for a simple environment setup and management.
Download our project:
git clone https://github.com/ZhaoningYu1996/MotifPiece.git
cd MotifPiece
Create a virtual environment with requirement packages:
conda env create -f environment.yml
Activate the virtual environment:
conda activate motifpiece
To reproduce the results of running single dataset from the paper:
python main.py --data_name --threshold --score_method --merge_method --decomposition_method
To reproduce the results of cross datasets learning from the paper:
python cross_dataset_mol.py # Datasets in MoleculeNet
python cross_dataset_ptc.py # PTC datasets
To apply MotifPiece and extract motifs on a personal SMILES representation dataset, you can use MotifPiece class in motifpiece.py:
motifpiece = MotifPiece(*args)
In *args
, you can setup threshold
, score_method
, and merge_method
. You can also setup train_indices
to only extract motifs from the training set.