Metadata-Version: 2.1
Name: chemicalx
Version: 0.0.9
Summary: A Deep Learning Library for Drug Pair Scoring.
Home-page: https://github.com/AstraZeneca/chemicalx
Author: Benedek Rozemberczki and Charles Hoyt
Author-email: benedek.rozemberczki@gmail.com
License: Apache License, Version 2.0
Download-URL: https://github.com/AstraZeneca/chemicalx/archive/v0.0.9.tar.gz
Description: [pypi-image]: https://badge.fury.io/py/chemicalx.svg
        [pypi-url]: https://pypi.python.org/pypi/chemicalx
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        [size-url]: https://github.com/AstraZeneca/chemicalx/archive/main.zip
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        [build-url]: https://github.com/AstraZeneca/chemicalx/actions?query=workflow%3ACI
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        [docs-url]: https://chemicalx.readthedocs.io/en/latest/?badge=latest
        [coverage-image]: https://codecov.io/gh/AstraZeneca/chemicalx/branch/main/graph/badge.svg
        [coverage-url]: https://codecov.io/github/AstraZeneca/chemicalx?branch=main
        
        <p align="center">
          <img width="90%" src="https://github.com/AstraZeneca/chemicalx/blob/main/images/chemicalx_logo.jpg?sanitize=true" />
        </p>
        
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        [![PyPI Version][pypi-image]][pypi-url]
        [![Docs Status][docs-image]][docs-url]
        [![Code Coverage][coverage-image]][coverage-url]
        [![Build Status][build-image]][build-url]
        
        **[Documentation](https://chemicalx.readthedocs.io)** | **[External Resources](https://chemicalx.readthedocs.io/en/latest/notes/resources.html)** | **[Datasets](https://chemicalx.readthedocs.io/en/latest/notes/introduction.html#datasets)** | **[Examples](https://github.com/AstraZeneca/chemicalx/tree/main/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](https://arxiv.org/pdf/2111.02916v4.pdf). 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](https://arxiv.org/abs/2111.02916) 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.
        
        <p align="center">
          <img width="90%" src="https://github.com/AstraZeneca/chemicalx/blob/main/images/pair_scoring.jpg?sanitize=true" />
        </p>
        
        
        **Getting Started**
        
        The API of `chemicalx` provides a high-level function for training and evaluating models
        that's heavily influenced by the [PyKEEN](https://github.com/pykeen/pykeen/)
        training and evaluation pipeline:
        
        ```python
        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/")
        ```
        
        --------------------------------------------------------------------------------
        
        **Case Study Tutorials**
        
        We provide in-depth case study like tutorials in the [Documentation](https://chemicalx.readthedocs.io/en/latest/), 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:
        
        ```bibtex
        @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](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.deepddi.DeepDDI)** from [Deep Learning Improves Prediction of Drug–Drug and Drug–Food Interactions](https://www.pnas.org/content/115/18/E4304) (PNAS)
        
        * **[DeepSynergy](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.deepsynergy.DeepSynergy)** from [DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning](https://academic.oup.com/bioinformatics/article/34/9/1538/4747884) (Bioinformatics)
        
        **2019**
        
        * **[MR-GNN](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.mrgnn.MRGNN)** from [MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions](https://arxiv.org/abs/1905.09558) (IJCAI)
        
        * **[MHCADDI](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.mhcaddi.MHCADDI)** from [Drug-Drug Adverse Effect Prediction with Graph Co-Attention](https://arxiv.org/pdf/1905.00534v1.pdf) (ICML)
        
        **2020**
        
        * **[CASTER](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.caster.CASTER)** from [CASTER: Predicting Drug Interactions with Chemical Substructure Representation](https://arxiv.org/abs/1911.06446) (AAAI)
        
        * **[SSI-DDI](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.ssiddi.SSIDDI)** from [SSI–DDI: Substructure–Substructure Interactions for Drug–Drug Interaction Prediction](https://academic.oup.com/bib/article-abstract/22/6/bbab133/6265181) (Briefings in Bioinformatics)
        
        * **[EPGCN-DS](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.epgcnds.EPGCNDS)** from [Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets](https://ojs.aaai.org/index.php/AAAI/article/view/7236) (AAAI)
        
        * **[DeepDrug](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.deepdrug.DeepDrug)** from [DeepDrug: A General Graph-Based Deep Learning Framework for Drug Relation Prediction](https://europepmc.org/article/ppr/ppr236757) (PMC)
        
        * **[GCN-BMP](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.gcnbmp.GCNBMP)** from [GCN-BMP: Investigating graph representation learning for DDI prediction task](https://www.sciencedirect.com/science/article/pii/S1046202320300608) (Methods)
        
        **2021**
        
        * **[DeepDDS](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.deepdds.DeepDDS)** from [DeepDDS: Deep Graph Neural Network with Attention Mechanism to Predict Synergistic Drug Combinations](https://arxiv.org/abs/2107.02467) (Briefings in Bioinformatics)
        
        * **[MatchMaker](https://chemicalx.readthedocs.io/en/latest/modules/root.html#chemicalx.models.matchmaker.MatchMaker)** from [MatchMaker: A Deep Learning Framework for Drug Synergy Prediction](https://pubmed.ncbi.nlm.nih.gov/34086576/) (ACM TCBB)
        
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        Head over to our [documentation](https://chemicalx.readthedocs.io) 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](https://chemicalx.readthedocs.io) in the `examples/` directory.
        
        If you notice anything unexpected, please open an [issue](github.com/AstraZeneca/chemicalx/issues). If you are missing a specific method, feel free to open a [feature request](https://github.com/AstraZeneca/chemicalx/issues).
        
        
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        **Installation**
        
        **PyTorch 1.10.0**
        
        To install for PyTorch 1.10.0, simply run
        
        ```sh
        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**   | ✅    |         |         |
        
        
        --------------------------------------------------------------------------------
        
        **Running tests**
        
        ```
        $ python setup.py test
        ```
        --------------------------------------------------------------------------------
        
        **License**
        
        - [Apache 2.0 License](https://github.com/AstraZeneca/chemicalx/blob/main/LICENSE)
        
Keywords: drug,deep-learning,deep-chemistry,deep-ai,torch-drug,synergy-prediction,synergy,drug-combination,deep-synergy,drug-interaction,chemistry,pharma
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
