{ "info": { "author": "Lars Ridder", "author_email": "lars.ridder@esciencecenter.nl>", "bugtrack_url": null, "classifiers": [ "Environment :: Console", "Intended Audience :: Science/Research", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "Topic :: Scientific/Engineering :: Chemistry" ], "description": "SyGMa\n=====\nSyGMa is a python library for the **Sy**\\ stematic **G**\\ eneration of potential **M**\\ et\\ **a**\\ bolites.\nIt is a reimplementation of the metabolic rules outlined in\n`Ridder, L., & Wagener, M. (2008)\nSyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites.\nChemMedChem, 3(5), 821-832\n`_.\n\n.. image:: https://travis-ci.org/3D-e-Chem/sygma.svg?branch=master\n :target: https://travis-ci.org/3D-e-Chem/sygma\n.. image:: https://api.codacy.com/project/badge/Grade/7f18ab1d1a80437f8e28ac1676c70519\n :target: https://www.codacy.com/app/3D-e-Chem/sygma?utm_source=github.com&utm_medium=referral&utm_content=3D-e-Chem/sygma&utm_campaign=Badge_Grade\n.. image:: https://api.codacy.com/project/badge/Coverage/7f18ab1d1a80437f8e28ac1676c70519\n :target: https://www.codacy.com/app/3D-e-Chem/sygma?utm_source=github.com&utm_medium=referral&utm_content=3D-e-Chem/sygma&utm_campaign=Badge_Coverage\n.. image:: https://img.shields.io/badge/docker-ready-blue.svg\n :target: https://hub.docker.com/r/3dechem/sygma\n.. image:: https://anaconda.org/3d-e-chem/sygma/badges/installer/conda.svg\n :target: https://conda.anaconda.org/3d-e-chem\n\nRequirements\n------------\nSyGMa requires RDKit with INCHI support\n\nInstallation\n------------\n* Install with Anaconda: ``conda install -c 3d-e-Chem -c rdkit sygma``\n\nOR\n\n* Install RDKit following the instructions in http://www.rdkit.org/docs/Install.html\n\nAND\n\n* ``pip install sygma`` OR, after downloading sygma, ``python setup.py install``\n\nExample: generating metabolites of phenol\n-----------------------------------------\n.. code-block:: python\n\n import sygma\n from rdkit import Chem\n\n # Each step in a scenario lists the ruleset and the number of reaction cycles to be applied\n scenario = sygma.Scenario([\n [sygma.ruleset['phase1'], 1],\n [sygma.ruleset['phase2'], 1]])\n\n # An rdkit molecule, optionally with 2D coordinates, is required as parent molecule\n parent = Chem.MolFromSmiles(\"c1ccccc1O\")\n\n metabolic_tree = scenario.run(parent)\n metabolic_tree.calc_scores()\n\n print metabolic_tree.to_smiles()\n\n\nDocker\n------\nSyGMa can be executed in a Docker (https://www.docker.com/) container as follows:\n\n.. code-block:: bash\n\n docker run 3dechem/sygma c1ccccc1O", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/ridderl/sygma", "keywords": null, "license": "GPL", "maintainer": null, "maintainer_email": null, "name": "SyGMa", "package_url": "https://pypi.org/project/SyGMa/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/SyGMa/", "project_urls": { "Download": "UNKNOWN", "Homepage": "https://github.com/ridderl/sygma" }, "release_url": "https://pypi.org/project/SyGMa/1.1.0/", "requires_dist": null, "requires_python": null, "summary": "Systematic Generation of potential MetAbolites", "version": "1.1.0" }, "last_serial": 2626643, "releases": { "1.0": [ { "comment_text": "", "digests": { "md5": "5474eea1393f7a6670b5ccfa82bb5d1f", "sha256": "d679119349a3ca18fca29586b914ba539fa5ec99f6b73477c4f67ff5101646af" }, "downloads": -1, "filename": "SyGMa-1.0.tar.gz", "has_sig": false, "md5_digest": "5474eea1393f7a6670b5ccfa82bb5d1f", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 13624, "upload_time": "2017-01-18T15:39:30", "url": "https://files.pythonhosted.org/packages/ca/a9/f8ce3cc920fa5c0624734531ec487817453c831c25de54e2563f5fcd8103/SyGMa-1.0.tar.gz" } ], "1.0.1": [ { "comment_text": "", "digests": { "md5": "a797d73abd2680e1b22222006693e540", "sha256": "de14e034a73f6739bef713c5cdd4318dd41e2df71083909cd6885a18b115d896" }, "downloads": -1, "filename": "SyGMa-1.0.1.tar.gz", "has_sig": false, "md5_digest": "a797d73abd2680e1b22222006693e540", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 13681, "upload_time": "2017-02-02T16:27:31", "url": "https://files.pythonhosted.org/packages/62/9e/65b7fbb38f83e187d5af5db02ec7ae710b909644f6d7731ca5dd8ce13e2f/SyGMa-1.0.1.tar.gz" } ], "1.1.0": [ { "comment_text": "", "digests": { "md5": "0a398b1d2633e5153c8b588fd823232c", "sha256": "7daf933862c25851176086ee15ef9210bc1b73f7c0d9dc26e68be73c9c47b244" }, "downloads": -1, "filename": "SyGMa-1.1.0.tar.gz", "has_sig": false, "md5_digest": "0a398b1d2633e5153c8b588fd823232c", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 13809, "upload_time": "2017-02-07T22:58:28", "url": "https://files.pythonhosted.org/packages/01/0d/7960ca6076d00ff33bf2aa0ac925074a07820900b8b8ae1edae83d12e488/SyGMa-1.1.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "0a398b1d2633e5153c8b588fd823232c", "sha256": "7daf933862c25851176086ee15ef9210bc1b73f7c0d9dc26e68be73c9c47b244" }, "downloads": -1, "filename": "SyGMa-1.1.0.tar.gz", "has_sig": false, "md5_digest": "0a398b1d2633e5153c8b588fd823232c", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 13809, "upload_time": "2017-02-07T22:58:28", "url": "https://files.pythonhosted.org/packages/01/0d/7960ca6076d00ff33bf2aa0ac925074a07820900b8b8ae1edae83d12e488/SyGMa-1.1.0.tar.gz" } ] }