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feat: Initial open source release.
Signed-off-by: Matteo Manica <[email protected]>
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‎.gitignore

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# secrets detector
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.secrets.baseline
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# mac files
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.DS_Store
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# dotenv
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.env
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# virtualenv
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.venv
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venv/
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ENV/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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# data files
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.pdf
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.csv
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# swap files
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*swp
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# shell scripts
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*.sh
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# trained models
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/models

‎.travis.yml

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dist: trusty
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services:
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- docker
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before_script:
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- docker pull drugilsberg/rdkit-ubuntu:latest
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- docker build -f .travis/Dockerfile -t predictor .
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script:
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- docker run -it predictor python3 -c "import paccmann_predictor"
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- docker run -it predictor python3 examples/train_paccmann.py -h

‎.travis/Dockerfile

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FROM drugilsberg/rdkit-ubuntu:latest
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RUN apt-get update && apt-get install -y git
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WORKDIR /predictor
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# install requirements
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COPY examples/requirements.txt .
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RUN pip3 install --no-cache-dir -r requirements.txt
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# copy paccmann_predictor
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COPY . .
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RUN pip3 install --no-deps .
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CMD /bin/bash

‎LICENSE

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Copyright 2019 Ali Oskooei, Jannis Born, Matteo Manica, Joris Cadow
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

‎README.md

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# paccmann_predictor
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PyTorch implementation of PaccMann.
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IC50 prediction using drug properties and tissue-specific cell line (gene expression profiles).
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`paccmann_predictor` is a package for drug sensitivity prediction and is the core component of the repo.
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*NOTE*: PaccMann acronyms "Prediction of AntiCancer Compound sensitivity with Multi-modal Attention-based Neural Networks".
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*NOTE*: This repo contains the `pytorch` implementation of our best model architecture (a multiscale convolutional attentive SMILES encoder).
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*NOTE*: For details, please see our [paper](https://doi.org/10.1021/acs.molpharmaceut.9b00520) in *Molecular Pharmaceutics*.
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## Requirements
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- `conda>=3.7`
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## Installation
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The library itself has few dependencies (see [setup.py](setup.py)) with loose requirements.
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To run the example training script we provide environment files under `examples/`.
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Create a conda environment:
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```sh
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conda env create -f examples/conda.yml
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```
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Activate the environment:
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```sh
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conda activate paccmann_predictor
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```
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Install in editable mode for development:
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```sh
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pip install -e .
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```
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## Example usage
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In the `examples` directory is a training script [train_paccmann.py](./examples/train_paccmann.py) that makes use
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of `paccmann_predictor`.
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```console
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(paccmann_predictor) $ python examples/train_paccmann.py -h
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usage: train_paccmann.py [-h]
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train_sensitivity_filepath test_sensitivity_filepath
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gep_filepath smi_filepath gene_filepath
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smiles_language_filepath model_path params_filepath
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training_name
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positional arguments:
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train_sensitivity_filepath
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Path to the drug sensitivity (IC50) data.
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test_sensitivity_filepath
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Path to the drug sensitivity (IC50) data.
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gep_filepath Path to the gene expression profile data.
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smi_filepath Path to the SMILES data.
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gene_filepath Path to a pickle object containing list of genes.
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smiles_language_filepath
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Path to a pickle object a SMILES language object.
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model_path Directory where the model will be stored.
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params_filepath Path to the parameter file.
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training_name Name for the training.
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optional arguments:
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-h, --help show this help message and exit
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```
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`params_path` could point to [examples/example_params.json](examples/example_params.json) examples for other files can be downloaded from [here](https://ibm.box.com/v/paccmann-pytoda-data).
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## References
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If you use `paccmann_predictor` in your projects, please cite the following:
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```bib
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@article{doi:10.1021/acs.molpharmaceut.9b00520,
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author = {Manica, Matteo and Oskooei, Ali and Born, Jannis and Subramanian, Vigneshwari and Saez-Rodriguez, Julio and Rodriguez Martinez, Maria},
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title = {Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders},
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journal = {Molecular Pharmaceutics},
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year = {2019},
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doi = {10.1021/acs.molpharmaceut.9b00520},
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note ={PMID: 31618586},
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URL = {
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https://doi.org/10.1021/acs.molpharmaceut.9b00520
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},
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eprint = {
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https://doi.org/10.1021/acs.molpharmaceut.9b00520
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}
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}
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@misc{born2019reinforcement,
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title={Reinforcement learning-driven de-novo design of anticancer compounds conditioned on biomolecular profiles},
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author={Jannis Born and Matteo Manica and Ali Oskooei and Maria Rodriguez Martinez},
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year={2019},
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eprint={1909.05114},
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archivePrefix={arXiv},
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primaryClass={q-bio.BM}
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}
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```

‎examples/conda.yml

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name: paccmann_predictor
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channels:
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- https://conda.anaconda.org/rdkit
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dependencies:
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- rdkit=2019.03.1
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- python>=3.6,<3.8
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- pip>=19.1
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- pip:
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- pytoda @ git+https://github.com/PaccMann/[email protected]
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- numpy>=1.14.3
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- scipy>=1.3.1
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- tensorflow>=1.10.0,<2.0
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- torch==1.0.1

‎examples/example_params.json

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{
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"drug_sensitivity_min_max": true,
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"augment_smiles": true,
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"smiles_start_stop_token": true,
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"number_of_genes": 2128,
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"smiles_padding_length": 560,
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"stacked_dense_hidden_sizes": [
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1024,
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512
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],
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"activation_fn": "relu",
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"dropout": 0.5,
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"batch_norm": true,
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"filters": [
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64,
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64
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],
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"multiheads": [
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],
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"smiles_embedding_size": 16,
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"kernel_sizes": [
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[
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3,
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16
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],
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[
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],
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[
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]
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],
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"smiles_attention_size": 64,
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"embed_scale_grad": false,
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"final_activation": true,
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"gene_to_dense": false,
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"batch_size": 2048,
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"lr": 0.01,
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"optimizer": "adam",
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"loss_fn": "mse",
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"epochs": 200,
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"save_model": 25
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}

‎examples/requirements.txt

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pytoda @ git+https://github.com/PaccMann/[email protected]
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numpy>=1.14.3
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scipy>=1.3.1
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tensorflow>=1.10.0,<2.0
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torch==1.0.1

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