PIGNet2: A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening

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.

Installation

You can download this repository by git clone https://github.com/mseok/PIGNet2.git. Then, you can proceed with the following steps.

Requirements

Environment Setup

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
conda install rdkit=2022.03.4 openbabel pymol-open-source -c conda-forge

Install Dependencies

pip install -r requirements.txt

Data

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.

Training

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

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

Pre-trained Models

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.

Using PIGNet2 for a single data point

Note

We highly recommend to use SMINA-optimized ligand conformations and doing 4-model ensemble to get accurate results.

Prepare protein pdb file and ligand sdf. Execute the following command to generate the result in $OUTPUT path (the output path is predict.txt by default):

python src/exe/predict.py ./src/ckpt/pda_0.pt -p $PROTEIN -l $LIGAND -o $OUTPUT

By default, each element of result are named as $(basename $PROTEIN .pdb)_$(basename $LIGAND .sdf)_${idx}, where ${idx} is an index of ligand conformation.

Case 1: a single pdb and a single sdf with one conformation

python src/exe/predict.py ./src/ckpt/pda_0.pt -p examples/protein.pdb -l examples/ligand_single_conformation.sdf -o examples/case1.txt

Case 2: a single pdb and a single sdf with multiple conformations

src/exe/predict.py automatically enumerates all conformations in ligand sdf.

python src/exe/predict.py ./src/ckpt/pda_0.pt -p examples/protein.pdb -l examples/ligand1.sdf -o examples/case2.txt

Case 3: a single pdb and multiple sdfs with multiple conformations

src/exe/predict.py automatically make protein-ligand pairs for a single pdb and all ligand sdfs.

python src/exe/predict.py ./src/ckpt/pda_0.pt -p examples/protein.pdb -l examples/ligand1.sdf examples/ligand2.sdf -o examples/case3.txt

Case 4: multiple pdbs and multiple sdfs with multiple conformations

In this case, you should match the order of ligand and protein files and all of them sequentially. For example, if you have protein1-ligand1, protein1-ligand2, protein2-ligand3, you should do like following:

python src/exe/predict.py ./src/ckpt/pda_0.pt -p protein1.pdb protein1.pdb protein2.pdb -l ligand1.sdf ligand2.sdf ligand3.sdf