InteractionGraphNet(IGN)

a Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions. Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning-based methods based on hand-crafted descriptors, one-dimensional protein sequences and/or twodimensional graph representations limit their capability to learn the generalized molecular interactions in 3D space. Here we proposed a novel deep graph representation learning framework named InteractionGraphNet (IGN) to learn the protein-ligand interaction patterns from the 3D structures of protein-ligand complexes in an end-to-end manner. In IGN, two independent graph convolution modules were stacked to sequentially learn the intramolecular and intermolecular interactions, and only the readouts from the intermolecular convolution module were accepted to force IGN to capture the protein-ligand interactions in 3D space. Extensive binding affinity prediction, large-scale structure-based virtual screening and pose prediction experiments demonstrated that IGN achieved better or competitive performance against other state-of-the-art ML-based baselines and docking programs, highlighting the great superiority of IGN compared to the other baselines. More importantly, such state-of-the-art performance was proved from the successful generalization of truly learning protein-ligand interaction patterns instead of just memorizing certain biased patterns from proteins or ligands. This source code was tested on the basic environment with conda==4.5.4 and cuda==11.0

Conda Environment Reproduce

Two methods were provided for reproducing the conda environment used in this paper

• create environment using file packaged by conda-pack

  Download the packaged file dgl430_v1.tar.gz and following commands can be used:

  mkdir /opt/conda_env/dgl430_v1
  tar -xvf dgl430_v1.tar.gz -C /opt/conda_env/dgl430_v1
  source activate /opt/conda_env/dgl430_v1
  conda-unpack

• create environment using files provided in ./envs directory

  The following commands can be used:

  conda create --prefix=/opt/conda_env/dgl430_v1 --file conda_packages.txt
  source activate /opt/conda_env/dgl430_v1
  pip install torch==1.3.1+cu92 torchvision==0.4.2+cu92 -f https://download.pytorch.org/whl/torch_stable.html
  pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
  pip install -r pip_packages.txt

Usage

Users can directly use our well-trained model (depoisted in ./model_save/ directory) to predict the binding affinityies of protein-ligand complexes. Other functions including pose prediction, structure-based virtual screening, and train the customized binding model is available in the future.

• step 1: Clone the Repository

git clone https://github.com/zjujdj/IGN.git

• step 2: Construction of Conda Environment

# method1 in 'Conda Environment Reproduce' section
mkdir /opt/conda_env/dgl430_v1
tar -xvf dgl430_v1.tar.gz -C /opt/conda_env/dgl430_v1
source activate /opt/conda_env/dgl430_v1
conda-unpack

# method2 in 'Conda Environment Reproduce' section
cd ./IGN/envs
conda create --prefix=/opt/conda_env/dgl430_v1 --file conda_packages.txt
source activate /opt/conda_env/dgl430_v1
pip install torch==1.3.1+cu92 torchvision==0.4.2+cu92 -f https://download.pytorch.org/whl/torch_stable.html
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install -r pip_packages.txt

• step 3: Binding Affinity Prediction

cd ./IGN/scripts
python3 model_ign_prediction.py --test_file_path='../input_data/user1'

• step 4: Other functions

will see you soon

Acknowledgement

Some scripts were based on the dgl project. We'd like to show our sincere thanks to them.