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