Pocket2Drug is an encoder-decoder deep neural network that predicts binding drugs given protein binding sites (pockets). The pocket graphs are generated using Graphsite. The encoder is a graph neural network, and the decoder is a recurrent neural network. The SELFIES molecule representation is used as the tokenization scheme instead of SMILES. The pipeline of Pocket2Drug is illustrated below:
- Pytorch
- Pytorch-geometric
- Rdkit
- SELFIES
- Pandas
- BioPandas
- Numpy
- Scipy
All the related data can be downloaded here. There are two dataset files:
- dataset.tar.gz: contains all binding site data in this project.
- pops.tar.gz: contains information of node feature contact surface area.
The configurations for training can be updated in train.yaml. Modify the pocket_dir and pop_dir entries to the paths of the extracted dataset. Modify the out_dir entry to the folder where you want to save the output results. Then,
python train.py
After training, the trained model will be saved at out_dir, and we can use it to sample predicted molecules:
python sample.py -batch_size 1024 -num_batches 1 -pocket_dir path_to_dataset_folder -popsa_dir path_to_pops_folder