PlaNet: Predicting population response to drugs via clinical knowledge graph

PyTorch implementation of PlaNet, a geometric deep learning tool for predicting population response to drugs. PlaNet provides a new clinical knowledge graph that captures the relations between disease biology, drug chemistry, and population characteristics. Using this knowledge graph, PlaNet can take a population and drugs to be applied (e.g., a clinical trial) as an input and predict the efficacy and safety of the drugs for the population. For a detailed description of the algorithm, please see our manuscript "Predicting population response to drugs via clinical knowledge graph".

1. Installation

Environment

Run the following commands to create a conda environment:

conda create -n planet python=3.8
source activate planet

pip install torch==1.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip install transformers==4.4.2 datasets==2.7.1 tensorboard==2.11.0 pandas wandb sklearn seqeval matplotlib pyyaml seaborn anndata scanpy
pip install setuptools==58.2.0 numpy==1.22.2

pip install torch-scatter==2.0.9 torch-sparse==0.6.12 -f https://pytorch-geometric.com/whl/torch-1.10.1+cu113.html
pip install torch-geometric==2.0.0
pip install ogb==1.3.0 --no-deps

After this installation, the tokenizers library version should be 0.10.3. In case you encounter an error about the tokenizers version, go to <conda_path>/envs/<env_name>/lib/python3.8/site-packages/transformers/dependency_versions_check.py and replace the if pkg == "tokenizers": ... block with if pkg == "tokenizers": continue.

The total install time should be within 10 minutes.

Data

You can download all the data (knowledge graph, clinical trial dataset, models, etc.) from here (data.zip). Unzip this, which will create a ./data directory.

2. Demo

We provide a demo notebook for loading the PlaNet knowledge graph and clinical trials data, and running the PlaNet models:

notebooks/demo.ipynb

The expected run time should be ~10 minutes.

3. Model training

Go to ./gcn_models directory. We train models to predict the efficacy, safety, and potential adverse events of a clinical trial.

To train a model for efficacy prediction, run commands in

../scripts/train_efficacy.sh

To train a model for safety prediction, run commands in

../scripts/train_safety.sh

To train a model for adverse event prediction, run commands in

../scripts/train_ae.sh

Citation

If you find our code and research useful, please consider citing:

@article{planet2023,
  author =  {Maria Brbi{\'c} and Michihiro Yasunaga and Prabhat Agarwal and Jure Leskovec},
  title =   {Predicting population response to drugs via clinical knowledge graph},
  year =    {2023},  
}