https://pubs.acs.org/doi/10.1021/acs.jcim.9b00387

python -u train.py --dropout_rate=0.3 --epoch=1000 --ngpu=1 --batch_size=256 --num_workers=0

We added only about data of 1000 samples in the data folder due to the size of the dataset so the performance is much lower than the paper. Each sample is saved in a pickle file and it consists of two rdkit objects of a ligand and protein. The inputs of the neural network are processed in the dataset class on the fly.