Documentation | External Resources | Datasets | Examples
ChemicalX is a deep learning library for drug-drug interaction, polypharmacy side effect and synergy prediction. The library consists of data loaders and integrated benchmark datasets. It also includes state-of-the-art deep neural network architectures that solve the drug pair scoring task. Implemented methods cover traditional SMILES string based techniques and neural message passing based models.
Drug Pair Scoring Explained
Our framework solves the so called drug pair scoring task of computational chemistry. In this task a machine learning model has to predict the outcome of administering two drugs together in a biological or chemical context. Deep learning models which solve this task have an architecture with two distinctive parts:
- A drug encoder layer which takes a pair of drugs as an input (blue and red drugs below).
- A head layer which outputs scores in the administration context - polypharmacy in our explanatory figure.
Case Study Tutorials
We provide in-depth case study tutorials in the Documentation, each covers an aspect of ChemicalX’s functionality.
Citing
If you find ChemicalX and the new datasets useful in your research, please consider adding the following citation:
@inproceedings{chemicalx,
author = {Benedek Rozemberczki and Charles Tapley Hoyt and Benjamin Gyori},
title = {{ChemicalX: A Deep Learning Library fo Drug Pair Scoring}},
year = {2022},
}
A simple example
Methods Included
In detail, the following temporal graph neural networks were implemented.
2017
- DeepCCI from DeepCCI: End-to-end Deep Learning for Chemical-Chemical Interaction Prediction (ACM BCB)
2018
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DeepDDI from Deep Learning Improves Prediction of Drug–Drug and Drug–Food Interactions (PNAS)
-
DeepSynergy from DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning (Bioinformatics)
2019
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MR-GNN from MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions (IJCAI)
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MHCADDI from Drug-Drug Adverse Effect Prediction with Graph Co-Attention (ICML)
2020
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CASTER from CASTER: Predicting Drug Interactions with Chemical Substructure Representation (AAAI)
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SSI-DDI from SSI–DDI: Substructure–Substructure Interactions for Drug–Drug Interaction Prediction (Briefings in Bioinformatics)
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EPGCN-DS from Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets (AAAI)
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AuDNNSynergy from Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models (Methods in Molecular Biology)
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DeepDrug from DeepDrug: A General Graph-Based Deep Learning Framework for Drug Relation Prediction (PMC)
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GCN-BMP from GCN-BMP: Investigating graph representation learning for DDI prediction task (Methods)
2021
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DPDDI from DPDDI: a Deep Predictor for Drug-Drug Interactions (BMC Bioinformatics)
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DeepDDS from DeepDDS: Deep Graph Neural Network with Attention Mechanism to Predict Synergistic Drug Combinations (Briefings in Bioinformatics)
-
MatchMaker from MatchMaker: A Deep Learning Framework for Drug Synergy Prediction (ACM TCBB)
Auxiliary Layers
Head over to our documentation to find out more about installation, creation of datasets and a full list of implemented methods and available datasets. For a quick start, check out the examples in the examples/ directory.
If you notice anything unexpected, please open an issue. If you are missing a specific method, feel free to open a feature request.
Installation
Binaries are provided for Python version <= 3.9.
PyTorch 1.9.0
To install the binaries for PyTorch 1.9.0, simply run
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.9.0+${CUDA}.html
pip install torch-geometric
pip install chemicalx
where ${CUDA} should be replaced by either cpu, cu102, or cu111 depending on your PyTorch installation.
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Expand to see installation guides for older PyTorch versions...
PyTorch 1.8.0
To install the binaries for PyTorch 1.8.0, simply run
$ pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.8.0+${CUDA}.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal
where ${CUDA} should be replaced by either cpu, cu101, cu102, or cu111 depending on your PyTorch installation.
cpu |
cu101 |
cu102 |
cu111 |
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| Linux |
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| Windows |
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PyTorch 1.7.0
To install the binaries for PyTorch 1.7.0, simply run
$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.7.0.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal
where ${CUDA} should be replaced by either cpu, cu92, cu101, cu102 or cu110 depending on your PyTorch installation.
cpu |
cu92 |
cu101 |
cu102 |
cu110 |
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| Linux |
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| Windows |
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PyTorch 1.6.0
To install the binaries for PyTorch 1.6.0, simply run
$ pip install torch-scatter==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-sparse==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-cluster==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-spline-conv==latest+${CUDA} -f https://pytorch-geometric.com/whl/torch-1.6.0.html
$ pip install torch-geometric
$ pip install torch-geometric-temporal
where ${CUDA} should be replaced by either cpu, cu92, cu101 or cu102 depending on your PyTorch installation.
cpu |
cu92 |
cu101 |
cu102 |
|
|---|---|---|---|---|
| Linux |
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| Windows |
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| macOS |
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Running tests
$ python setup.py test
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