https://github.com/paccmann/paccmann_datasets

pytoda - PaccMann PyTorch Dataset Classes. Read the docs: https://paccmann.github.io/paccmann_datasets/
https://github.com/paccmann/paccmann_datasets

bioinformatics chemoinformatics deep-learning python pytorch rdkit smiles

Last synced: about 2 months ago
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pytoda - PaccMann PyTorch Dataset Classes. Read the docs: https://paccmann.github.io/paccmann_datasets/

• Host: GitHub
• URL: https://github.com/paccmann/paccmann_datasets
• Owner: PaccMann
• License: mit
• Created: 2019-11-01T10:18:05.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-10-06T10:21:03.000Z (8 months ago)
• Last Synced: 2025-04-09T21:16:33.952Z (about 2 months ago)
• Topics: bioinformatics, chemoinformatics, deep-learning, python, pytorch, rdkit, smiles
• Language: Python
• Homepage:
• Size: 6.01 MB
• Stars: 28
• Watchers: 4
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PyToDa

[![PyPI version](https://badge.fury.io/py/pytoda.svg)](https://badge.fury.io/py/pytoda)

[![build](https://github.com/PaccMann/paccmann_datasets/workflows/build/badge.svg)](https://github.com/PaccMann/paccmann_datasets/actions)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

[![Downloads](https://static.pepy.tech/badge/pytoda)](https://pepy.tech/project/pytoda)

[![Downloads](https://static.pepy.tech/badge/pytoda/month)](https://pepy.tech/project/pytoda)

[![GitHub Super-Linter](https://github.com/PaccMann/paccmann_datasets/workflows/style/badge.svg)](https://github.com/marketplace/actions/super-linter)

## Overview

pytoda - PaccMann P*yTo*rch *Da*taset Classes

A python package that eases handling biochemical data for deep learning applications

with pytorch.

## Installation

`pytoda` ships via [PyPI](https://pypi.org/project/pytoda):

```sh

pip install pytoda

```

## Documentation

Please find the full documentation [here](https://paccmann.github.io/paccmann_datasets/).

## Development

For development setup, we recommend to work in a dedicated conda environment:

```sh

conda env create -f conda.yml

```

Activate the environment:

```sh

conda activate pytoda

```

Install in editable mode:

```sh

pip install -r dev_requirements.txt

pip install --user --no-use-pep517 -e .

```

## Examples

For some examples on how to use `pytoda` see [here](./examples)

## References

If you use `pytoda` in your projects, please cite the following:

```bib

@article{born2021data,

  title={Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2},

  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},

  journal={Machine Learning: Science and Technology},

  volume={2},

  number={2},

  pages={025024},

  year={2021},

  publisher={IOP Publishing}

}

```