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https://github.com/AstraZeneca/kallisto

Efficiently calculate 3D-features for quantitative structure-activity relationship approaches.
https://github.com/AstraZeneca/kallisto

chemistry computational-chemistry machinelearning quantum-chemistry

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Efficiently calculate 3D-features for quantitative structure-activity relationship approaches.

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README 

        


Kallisto




##



![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kallisto)

[![Documentation](https://img.shields.io/badge/GitBook-Docu-lightgrey)](https://ehjc.gitbook.io/kallisto/)

[![Maturity Level](https://img.shields.io/badge/Maturity%20Level-Under%20Development-orange)](https://img.shields.io/badge/Maturity%20Level-Under%20Development-orange)

[![Tests](https://github.com/AstraZeneca/kallisto/workflows/Tests/badge.svg)](https://github.com/AstraZeneca/kallisto/actions?workflow=Tests)

[![codecov](https://codecov.io/gh/AstraZeneca/kallisto/branch/master/graph/badge.svg?token=HI0U0R96X8)](https://codecov.io/gh/AstraZeneca/kallisto)

[![status](https://joss.theoj.org/papers/16126cbcfb826bf4810d243a009a6b02/status.svg)](https://joss.theoj.org/papers/16126cbcfb826bf4810d243a009a6b02)



# Table of Contents



- Full Author List

- Introduction

- Installation

- Testing suite

- Reference



# Full Author List



- Developer [Eike Caldeweyher](https://scholar.google.com/citations?user=25n8C3wAAAAJ&hl)

- Developer [Rocco Meli](https://scholar.google.com/citations?hl=de&user=s8cVcvYAAAAJ)

- Developer [Philipp Pracht](https://scholar.google.com/citations?user=PJiGPk0AAAAJ&hl)



# Introduction



We developed the `kallisto` program for the efficient and robust calculation of atomic features using molecular geometries either in a `xmol` or a `Turbomole` format.

Furthermore, several modelling tools are implemented, e.g., to calculate root-mean squared deviations via quaternions (including rotation matrices), sorting of molecular geometries and many more. All features of `kallisto` are described in detail within our [documentation](https://ehjc.gitbook.io/kallisto/) ([GitBook repository](https://github.com/f3rmion/gitbook-kallisto)).



## Main dependencies



```bash

click 7.1.2 Composable command line interface toolkit

numpy 1.20.1 NumPy is the fundamental package for array computing with Python.

scipy 1.6.0 SciPy: Scientific Library for Python

└── numpy >=1.16.5

```



For a list of all dependencies have a look at the pyproject.toml file.



## Installation from PyPI



To install `kallisto` via `pip` use our published PyPI package



```bash

pip install kallisto

```



## Installation from Source



Requirements to install `kallisto`from sources:



- [poetry](https://python-poetry.org/docs/#installation)

- [pyenv](https://github.com/pyenv/pyenv#installation) or [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)

- python >=3.7



First check that `poetry` is running correctly (v1.0.10 at the time of writing)



```bash

> poetry --version

Poetry version 1.0.10

```



Create a virtual environment (via `pyenv` or `conda`) and activate it. Afterwards, clone the `kallisto` project from GitHub and install it using `poetry`



```bash

> git clone [email protected]:AstraZeneca/kallisto.git

> cd kallisto

> poetry install

```



## Testing suite



The `kallisto` project uses [nox](https://nox.thea.codes/en/stable/tutorial.html#installation) as an automated unit test suite, which is therefore an additional dependency.



### Default nox session



The default session includes: linting (lint), type checks (mypy, pytype), and unit tests (tests).



```bash

> nox

```



When everything runs smoothly through, you are ready to go! After one successful nox run, we can reuse the created virtual environment via the `-r` flag.



```bash

> nox -r

```



Different unit test sessions are implemented (check the noxfile.py). They can be called separately via the run session `-rs` flag.



### Tests



Run all unit tests that are defined in the /tests directory.



```bash

> nox -rs tests

```



### Lint



`kallisto` uses the [flake8](https://flake8.pycqa.org/en/latest/) linter (check the .flake8 config file).



```bash

> nox -rs lint

```



### Black



`kallisto` uses the [black](https://github.com/psf/black) code formatter.



```bash

> nox -rs black

```



### Safety



`kallisto` checks the security of dependencies via [safety](https://pyup.io/safety/).



```bash

> nox -rs safety

```



### Mypy



`kallisto` checks for static types via [mypy](https://github.com/python/mypy) (check the mypy.ini config file).



```bash

> nox -rs mypy

```



### Pytype



`kallisto` furthermore uses [pytype](https://github.com/google/pytype) for type checks.



```bash

> nox -rs pytype

```



### Coverage



Unit test [coverage](https://coverage.readthedocs.io/en/coverage-5.4/) can be checked as well.



```bash

> nox -rs coverage

```



## Reference



Always cite:



Eike Caldeweyher, J. Open Source Softw., _2021_, 6, 3050. DOI: [10.21105/joss.03050](https://doi.org/10.21105/joss.03050)



```

@article{Caldeweyher2021,

  doi = {10.21105/joss.03050},

  url = {https://doi.org/10.21105/joss.03050},

  year = {2021},

  volume = {6},

  number = {60},

  pages = {3050},

  author = {Eike Caldeweyher},

  title = {kallisto: A command-line interface to simplify computational modelling and the generation of atomic features},

  journal = {J. Open Source Softw.}

}

```