pytoda: PaccMann PyTorch Dataset Classes.
Project description
PyToDa
Overview
pytoda - PaccMann PyTorch Dataset Classes
A python package that eases handling biochemical data for deep learning applications with pytorch.
Installation
pytoda
ships via PyPI:
pip install pytoda
Documentation
Please find the full documentation here.
Development
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 .
Note on rdkit
vs rdkit-pypi
NOTE: The conda env ships with the official rdkit
implementation. But the pip
installation overwrites the rdkit package with the
community-contributed PyPI package
called rdkit-pypi
.
This is intentional because pytoda
is distributed via PyPI too and most users will
thus depend on rdkit-pypi
. Keep in mind that rdkit-pypi
might contain bugs or
be outdated wrt rdkit
. If developers experience issues with rdkit-pypi
,
they can temporarily uninstall rdkit-pypi
and will then fall back on using
the proper rdkit
package.
Examples
For some examples on how to use pytoda
see here
References
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}
}
}
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