setup.cfg

[metadata]
name = chemprop
version = 1.7.0
author = The Chemprop Development Team (see LICENSE.txt)
author_email = chemprop@mit.edu
license = MIT
description = Molecular Property Prediction with Message Passing Neural Networks
keywords =
    chemistry
    machine learning
    property prediction
    message passing neural network
    graph neural network
url = https://github.com/chemprop/chemprop
download_url = https://github.com/chemprop/chemprop/v_1.7.0.tar.gz
long_description = file: README.md
long_description_content_type = text/markdown
classifiers =
    Programming Language :: Python :: 3.7
    Programming Language :: Python :: 3.8
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent
project_urls =
    Documentation = https://chemprop.readthedocs.io/en/latest/
    Source = https://github.com/chemprop/chemprop
    PyPi = https://pypi.org/project/chemprop/

[options]
packages = find:
install_requires =
    flask>=1.1.2,<=2.1.3
    Werkzeug<3
    hyperopt>=0.2.3
    matplotlib>=3.1.3
    numpy>=1.18.1
    pandas>=1.0.3
    pandas-flavor>=0.2.0
    scikit-learn>=0.22.2.post1
    scipy>=1.4.1
    sphinx>=3.1.2
    sphinx-rtd-theme>=2.0.0
    tensorboardX>=2.0
    torch>=1.4.0
    tqdm>=4.45.0
    typed-argument-parser>=1.6.1
    rdkit>=2020.03.1.0
    descriptastorus
python_requires = >=3.7,<3.9

[options.entry_points]
console_scripts =
    chemprop_train=chemprop.train:chemprop_train
    chemprop_predict=chemprop.train:chemprop_predict
    chemprop_fingerprint=chemprop.train:chemprop_fingerprint
    chemprop_hyperopt=chemprop.hyperparameter_optimization:chemprop_hyperopt
    chemprop_interpret=chemprop.interpret:chemprop_interpret
    chemprop_web=chemprop.web.run:chemprop_web
    sklearn_train=chemprop.sklearn_train:sklearn_train
    sklearn_predict=chemprop.sklearn_predict:sklearn_predict

[options.extras_require]
test = pytest>=6.2.2; parameterized>=0.8.1

[options.package_data]
chemprop = py.typed