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Datasets

To train the model from scratch, you need to download the preprocessed file from this link and save it into data/single or data/multi.

We use the CrossDocked dataset and reaction-based slicing method in LibINVENT to construct single and multi R-groups datasets. If you want to process the dataset from scratch, you can follow the steps:

  1. Download the dataset archive crossdocked_pocket10.tar.gz and the split file split_by_name.pt from this link.
  2. Extract the TAR archive using the command:
tar -xzvf crossdocked_pocket10.tar.gz
  1. Split raw PL-complexes and convert sdf files into SMILES format:
python split_and_convert.py
  1. Use the reaction-based slicing method in LibINVENT to slice the molecules into scaffolds and R-groups in Lib-INVENT-dataset and replace example_configurations/supporting_files/filter_conditions.json in Lib-INVENT-dataset with filter_conditions.json in this directory. For single R-group dataset, set the value of parameter max_cuts in example_configurations/reaction_based_slicing.json to 1 while for multi R-groups dataset to 4.
  2. Process and prepare datasets:
cd single
python -W ignore process_and_prepare.py
cd multi
python -W ignore process_and_prepare.py