chemicalx 0.0.9

A Deep Learning Library for Drug Pair Scoring.

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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.

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Drug Pair Scoring Explained

Our framework solves the 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:

1. A drug encoder layer which takes a pair of drugs as an input (blue and red drugs below).
2. A head layer which outputs scores in the administration context - polypharmacy in our explanatory figure.

Getting Started

The API of chemicalx provides a high-level function for training and evaluating models that's heavily influenced by the PyKEEN training and evaluation pipeline:

from chemicalx import pipeline
from chemicalx.models import DeepSynergy
from chemicalx.data import DrugCombDB

model = DeepSynergy(context_channels=112, drug_channels=256)
dataset = DrugCombDB()

results = pipeline(
    dataset=dataset,
    model=model,
    # Data arguments
    batch_size=5120,
    context_features=True,
    drug_features=True,
    drug_molecules=False,
    labels=True,
    # Training arguments
    epochs=100,
)

# Outputs information about the AUC-ROC, etc. to the console.
results.summarize()

# Save the model, losses, evaluation, and other metadata.
results.save("~/test_results/")

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Case Study Tutorials

We provide in-depth case study like tutorials in the Documentation, each covers an aspect of ChemicalX’s functionality.

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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},
}

Methods Included

In detail, the following drug pair scoring models were implemented.

2018

• 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

• MR-GNN from MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions (IJCAI)

• MHCADDI from Drug-Drug Adverse Effect Prediction with Graph Co-Attention (ICML)

2020

• CASTER from CASTER: Predicting Drug Interactions with Chemical Substructure Representation (AAAI)

• SSI-DDI from SSI–DDI: Substructure–Substructure Interactions for Drug–Drug Interaction Prediction (Briefings in Bioinformatics)

• EPGCN-DS from Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets (AAAI)

• DeepDrug from DeepDrug: A General Graph-Based Deep Learning Framework for Drug Relation Prediction (PMC)

• GCN-BMP from GCN-BMP: Investigating graph representation learning for DDI prediction task (Methods)

2021

• 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)

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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.

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Installation

PyTorch 1.10.0

To install for PyTorch 1.10.0, simply run

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+${CUDA}.html
pip install torchdrug
pip install chemicalx

where ${CUDA} should be replaced by either cpu, cu102, or cu111 depending on your PyTorch installation.

	cpu	cu102	cu111
Linux	✅	✅	✅
Windows	✅	✅	✅
macOS	✅

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Running tests

$ python setup.py test

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License

• Apache 2.0 License