pytoda - PaccMann PyTorch Dataset Classes
A python package that eases handling biochemical data for deep learning applications with pytorch.
pytoda
ships via PyPI:
pip install pytoda
Please find the full documentation here.
For development setup, we recommend to work in a dedicated conda environment:
conda env create -f conda.yml
Activate the environment:
conda activate pytoda
Install in editable mode:
pip install -r dev_requirements.txt
pip install --user --no-use-pep517 -e .
For some examples on how to use pytoda
see here
If you use pytoda
in your projects, please cite the following:
@article{born2021datadriven,
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
{Rodr{\'{i}}guez Mart{\'{i}}nez}, Mar{\'{i}}a
},
doi = {10.1088/2632-2153/abe808},
issn = {2632-2153},
journal = {Machine Learning: Science and Technology},
number = {2},
pages = {025024},
title = {{
Data-driven molecular design for discovery and synthesis of novel ligands:
a case study on SARS-CoV-2
}},
url = {https://iopscience.iop.org/article/10.1088/2632-2153/abe808},
volume = {2},
year = {2021}
}
@article{born2021paccmannrl,
title = {
PaccMann$^{RL}$: De novo generation of hit-like anticancer molecules from
transcriptomic data via reinforcement learning
},
journal = {iScience},
volume = {24},
number = {4},
year = {2021},
issn = {2589-0042},
doi = {https://doi.org/10.1016/j.isci.2021.102269},
url = {https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6},
author = {
Jannis Born and Matteo Manica and Ali Oskooei and Joris Cadow and Greta Markert
and Mar{\'\i}a Rodr{\'\i}guez Mart{\'\i}nez}
}
}