{"id":15118738,"url":"https://github.com/AstraZeneca/kallisto","last_synced_at":"2025-09-28T01:30:41.908Z","repository":{"id":47108835,"uuid":"333407935","full_name":"AstraZeneca/kallisto","owner":"AstraZeneca","description":"Efficiently calculate 3D-features for quantitative structure-activity relationship approaches.","archived":false,"fork":false,"pushed_at":"2024-02-01T10:34:54.000Z","size":456,"stargazers_count":62,"open_issues_count":2,"forks_count":21,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-01-09T06:22:10.197Z","etag":null,"topics":["chemistry","computational-chemistry","machinelearning","quantum-chemistry"],"latest_commit_sha":null,"homepage":"https://ehjc.gitbook.io/kallisto/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AstraZeneca.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-01-27T12:08:57.000Z","updated_at":"2024-10-06T23:40:58.000Z","dependencies_parsed_at":"2023-01-23T09:15:10.951Z","dependency_job_id":"43ecc83e-c032-4a0f-9455-909a2b25df17","html_url":"https://github.com/AstraZeneca/kallisto","commit_stats":{"total_commits":105,"total_committers":7,"mean_commits":15.0,"dds":"0.21904761904761905","last_synced_commit":"c1179f3969b814879b75bc5f9782161a4e4fd3d6"},"previous_names":[],"tags_count":9,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fkallisto","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fkallisto/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fkallisto/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AstraZeneca%2Fkallisto/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AstraZeneca","download_url":"https://codeload.github.com/AstraZeneca/kallisto/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":234475315,"owners_count":18839358,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["chemistry","computational-chemistry","machinelearning","quantum-chemistry"],"created_at":"2024-09-26T01:53:37.316Z","updated_at":"2025-09-28T01:30:36.329Z","avatar_url":"https://github.com/AstraZeneca.png","language":"Python","funding_links":[],"categories":["Ranked by starred repositories"],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"./assets/logo.svg\" alt=\"Kallisto\" width=\"300\"\u003e\n\u003c/div\u003e\n\n##\n\n![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kallisto)\n[![Documentation](https://img.shields.io/badge/GitBook-Docu-lightgrey)](https://ehjc.gitbook.io/kallisto/)\n[![Maturity Level](https://img.shields.io/badge/Maturity%20Level-Under%20Development-orange)](https://img.shields.io/badge/Maturity%20Level-Under%20Development-orange)\n[![Tests](https://github.com/AstraZeneca/kallisto/workflows/Tests/badge.svg)](https://github.com/AstraZeneca/kallisto/actions?workflow=Tests)\n[![codecov](https://codecov.io/gh/AstraZeneca/kallisto/branch/master/graph/badge.svg?token=HI0U0R96X8)](https://codecov.io/gh/AstraZeneca/kallisto)\n[![status](https://joss.theoj.org/papers/16126cbcfb826bf4810d243a009a6b02/status.svg)](https://joss.theoj.org/papers/16126cbcfb826bf4810d243a009a6b02)\n\n# Table of Contents\n\n- Full Author List\n- Introduction\n- Installation\n- Testing suite\n- Reference\n\n# Full Author List\n\n- Developer [Eike Caldeweyher](https://scholar.google.com/citations?user=25n8C3wAAAAJ\u0026hl)\n- Developer [Rocco Meli](https://scholar.google.com/citations?hl=de\u0026user=s8cVcvYAAAAJ)\n- Developer [Philipp Pracht](https://scholar.google.com/citations?user=PJiGPk0AAAAJ\u0026hl)\n\n# Introduction\n\nWe developed the `kallisto` program for the efficient and robust calculation of atomic features using molecular geometries either in a `xmol` or a `Turbomole` format.\nFurthermore, 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)).\n\n## Main dependencies\n\n```bash\nclick 7.1.2 Composable command line interface toolkit\nnumpy 1.20.1 NumPy is the fundamental package for array computing with Python.\nscipy 1.6.0 SciPy: Scientific Library for Python\n└── numpy \u003e=1.16.5\n```\n\nFor a list of all dependencies have a look at the pyproject.toml file.\n\n## Installation from PyPI\n\nTo install `kallisto` via `pip` use our published PyPI package\n\n```bash\npip install kallisto\n```\n\n## Installation from Source\n\nRequirements to install `kallisto`from sources:\n\n- [poetry](https://python-poetry.org/docs/#installation)\n- [pyenv](https://github.com/pyenv/pyenv#installation) or [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)\n- python \u003e=3.7\n\nFirst check that `poetry` is running correctly (v1.0.10 at the time of writing)\n\n```bash\n\u003e poetry --version\nPoetry version 1.0.10\n```\n\nCreate a virtual environment (via `pyenv` or `conda`) and activate it. Afterwards, clone the `kallisto` project from GitHub and install it using `poetry`\n\n```bash\n\u003e git clone git@github.com:AstraZeneca/kallisto.git\n\u003e cd kallisto\n\u003e poetry install\n```\n\n## Testing suite\n\nThe `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.\n\n### Default nox session\n\nThe default session includes: linting (lint), type checks (mypy, pytype), and unit tests (tests).\n\n```bash\n\u003e nox\n```\n\nWhen 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.\n\n```bash\n\u003e nox -r\n```\n\nDifferent unit test sessions are implemented (check the noxfile.py). They can be called separately via the run session `-rs` flag.\n\n### Tests\n\nRun all unit tests that are defined in the /tests directory.\n\n```bash\n\u003e nox -rs tests\n```\n\n### Lint\n\n`kallisto` uses the [flake8](https://flake8.pycqa.org/en/latest/) linter (check the .flake8 config file).\n\n```bash\n\u003e nox -rs lint\n```\n\n### Black\n\n`kallisto` uses the [black](https://github.com/psf/black) code formatter.\n\n```bash\n\u003e nox -rs black\n```\n\n### Safety\n\n`kallisto` checks the security of dependencies via [safety](https://pyup.io/safety/).\n\n```bash\n\u003e nox -rs safety\n```\n\n### Mypy\n\n`kallisto` checks for static types via [mypy](https://github.com/python/mypy) (check the mypy.ini config file).\n\n```bash\n\u003e nox -rs mypy\n```\n\n### Pytype\n\n`kallisto` furthermore uses [pytype](https://github.com/google/pytype) for type checks.\n\n```bash\n\u003e nox -rs pytype\n```\n\n### Coverage\n\nUnit test [coverage](https://coverage.readthedocs.io/en/coverage-5.4/) can be checked as well.\n\n```bash\n\u003e nox -rs coverage\n```\n\n## Reference\n\nAlways cite:\n\nEike Caldeweyher, J. Open Source Softw., _2021_, 6, 3050. DOI: [10.21105/joss.03050](https://doi.org/10.21105/joss.03050)\n\n```\n@article{Caldeweyher2021,\n  doi = {10.21105/joss.03050},\n  url = {https://doi.org/10.21105/joss.03050},\n  year = {2021},\n  volume = {6},\n  number = {60},\n  pages = {3050},\n  author = {Eike Caldeweyher},\n  title = {kallisto: A command-line interface to simplify computational modelling and the generation of atomic features},\n  journal = {J. Open Source Softw.}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAstraZeneca%2Fkallisto","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FAstraZeneca%2Fkallisto","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FAstraZeneca%2Fkallisto/lists"}