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PaccMann P*yTo*rch *Da*taset Classes\n\nA python package that eases handling biochemical data for deep learning applications\nwith pytorch.\n\n## Installation\n\n`pytoda` ships via [PyPI](https://pypi.org/project/pytoda):\n\n```sh\npip install pytoda\n```\n\n## Documentation\n\nPlease find the full documentation [here](https://paccmann.github.io/paccmann_datasets/).\n\n## Development\n\nFor development setup, we recommend to work in a dedicated conda environment:\n\n```sh\nconda env create -f conda.yml\n```\n\nActivate the environment:\n\n```sh\nconda activate pytoda\n```\n\nInstall in editable mode:\n\n```sh\npip install -r dev_requirements.txt\npip install --user --no-use-pep517 -e .\n```\n\n## Examples\n\nFor some examples on how to use `pytoda` see [here](./examples)\n\n## References\n\nIf you use `pytoda` in your projects, please cite the following:\n\n```bib\n@article{born2021data,\n  title={Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2},\n  author={Born, Jannis and Manica, Matteo and Cadow, Joris and Markert, Greta and Mill, Nil Adell and Filipavicius, Modestas and Janakarajan, Nikita and Cardinale, Antonio and Laino, Teodoro and Martinez, Maria Rodriguez},\n  journal={Machine Learning: Science and Technology},\n  volume={2},\n  number={2},\n  pages={025024},\n  year={2021},\n  publisher={IOP Publishing}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaccmann%2Fpaccmann_datasets","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpaccmann%2Fpaccmann_datasets","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaccmann%2Fpaccmann_datasets/lists"}