This repository is the official implementation of PIGNet2: A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening.

You can use conda or venv for environment setting. For the case of using conda, create the environment named pignet2 as following.

conda create -n pignet2 python=3.9
conda activate pignet2

pip install -r requirements.txt

Donwload our source data into dataset directory in this repository. By executing dataset/download.sh, you can download all the following datasets.

training dataset

• PDBbind v2020 scoring
• PDBbind v2020 docking
• PDBbind v2020 cross
• PDBbind v2020 random

benchmark dataset

• CASF-2016 socring
• CASF-2016 docking
• CASF-2016 screening
• DUD-E screening
• derivative benchmark

Then, you can extract the downloaded files by executing dataset/untar.sh.

The source codes for generating structures used in positive data augmentation is placed in generate_PDA directory.

Training scripts can be found in experiments/training_scripts directory. We provide 4 scripts for training.

• baseline.sh: training without any data augmentation
• only_nda.sh: training only with negative data augmentation
• only_pda.sh: training only with positive data augmentation
• pda_nda.sh: training with both positive and negative data augmentation

If you execute the script, the result files will be generated in your current working directory. By default, we recommend you to execute training scripts at experiemnts directory. All the result files are placed in outputs/${EXPERIMENT_NAME} directory.

Benchmark scripts can be found in experiments/benchmark_scripts directory. We provide 5 scripts for benchmark.

• casf2016_scoring.sh: benchmark on CASF-2016 scoring benchmark
• casf2016_docking.sh: benchmark on CASF-2016 docking benchmark
• casf2016_screening.sh: benchmark on CASF-2016 screening benchmark
• dude.sh: benchmark on DUD-E benchmark
• derivative.sh: benchmark on derivative benchmark (2015)

After training, you have to set the ${BENCHMARK_DIR} in each benchmark scripts, which is set as experiments/outputs/${EXPERIMENT_NAME} as default. Since experiments/outputs is set as a root directory of each experiment, it is highly recommended to place the outputs directory inside experiments directory. For using our pre-trained model for benchmark, please refer to the next section.

After that, you will get the benchmark result files in experiments/outputs/${EXPERIMENT_NAME}/benchmark. To benchmark each result files, you can execute src/benchmark/*.py. For example, you can perform DUD-E benchmark by the following command.

src/benchmark/dude_screening_power.py -f experiments/outputs/${EXPERIMENT_NAME}/benchmark/result_dude_${EPOCH}.txt -v

You can find the pre-trained models in src/ckpt. We provide PIGNet2 models trained with both positive and negative data augemntation, which is the best model. You can execute the experiments/benchmark/pretrained_*.sh scripts to get the benchmark results of pre-trained models. The scripts will generate result files in experiments/pretrained.