pytoda 1.1.4

pytoda: PaccMann PyTorch Dataset Classes.

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 .

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}
    }
}