{"id":20893208,"url":"https://github.com/keiserlab/e3fp-paper","last_synced_at":"2025-05-12T22:32:30.761Z","repository":{"id":53785891,"uuid":"62654200","full_name":"keiserlab/e3fp-paper","owner":"keiserlab","description":"3D molecular fingerprints (E3FP) paper repo","archived":false,"fork":false,"pushed_at":"2021-03-14T02:41:59.000Z","size":9875,"stargazers_count":14,"open_issues_count":2,"forks_count":2,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-11-03T03:26:59.453Z","etag":null,"topics":["3d","cheminformatics","e3fp","fingerprint","molecular","paper","python"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/keiserlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-07-05T16:57:30.000Z","updated_at":"2024-05-12T16:37:12.000Z","dependencies_parsed_at":"2022-09-15T13:50:08.806Z","dependency_job_id":null,"html_url":"https://github.com/keiserlab/e3fp-paper","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keiserlab%2Fe3fp-paper","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keiserlab%2Fe3fp-paper/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keiserlab%2Fe3fp-paper/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/keiserlab%2Fe3fp-paper/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/keiserlab","download_url":"https://codeload.github.com/keiserlab/e3fp-paper/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225156956,"owners_count":17429701,"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":["3d","cheminformatics","e3fp","fingerprint","molecular","paper","python"],"created_at":"2024-11-18T10:14:59.442Z","updated_at":"2024-11-18T10:14:59.527Z","avatar_url":"https://github.com/keiserlab.png","language":"Python","readme":"# Extended 3-Dimensional FingerPrint (E3FP) Paper Materials\n\n[E3FP](https://github.com/keiserlab/e3fp) is a computational method for\ngenerating 3D molecular fingerprints. This repository serves as an application\nof E3FP and contains a Python 2.7.x-compatible library and all scripts\nnecessary to reproduce the analyses and figures in the E3FP\npaper\u003csup\u003e[1](#axen2017)\u003c/sup\u003e.\n\n## Table of Contents\n- [Overview](#overview)\n- [Dependencies](#dependencies)\n    + [Required](#required)\n        - [Required for Library](#requiredlib)\n        - [Required for Project](#requiredproj)\n    + [Optional](#optional)\n- [Setup and Installation](#setup)\n- [Usage and Overview](#usage)\n- [References](#references)\n\n\u003ca name=\"overview\"\u003e\u003c/a\u003e\n## Overview\n\nThis repository is divided into two sections:\n\n- [`e3fp_paper`](e3fp_paper) is a Python library containing various classes\n  and methods used in the paper analysis. Specifically, it contains code for\n  interfacing with SeaWare, an implementation of the Similarity Ensemble\n  Approach (SEA), loading and saving SEA-compatible filetypes, running\n  *k*-fold cross-validation, and plotting the results.\n- [`project`](project) contains all scripts necessary to run the analyses in\n  the paper. While instructions are provided, please see the E3FP\n  paper\u003csup\u003e[1](#axen2017)\u003c/sup\u003e (preprint available) for detailed\n  explanations.\n\n\u003ca name=\"dependencies\"\u003e\u003c/a\u003e\n## Dependencies\n\n`e3fp_paper` is compatible with Python 2.7.x. It additionally has the following\ndependencies:\n\n\u003ca name=\"required\"\u003e\u003c/a\u003e\n### Required\n\n\u003ca name=\"requiredlib\"\u003e\u003c/a\u003e\n#### Required for Library\n\nThe following packages and their dependencies must be installed:\n\n- [e3fp](https://github.com/keiserlab/e3fp)\n- SeaWare\n- [RDKit](http://www.rdkit.org)\n- [NumPy](https://www.numpy.org)\n- [SciPy](https://www.scipy.org)\n- [Pandas](http://pandas.pydata.org)\n- [Matplotlib](http://matplotlib.org)\n- [scikit-learn](http://scikit-learn.org)\n- [nolearn](https://github.com/dnouri/nolearn)\n- [python_utilities](https://github.com/sdaxen/python_utilities)\n\n\u003ca name=\"requiredproj\"\u003e\u003c/a\u003e\n#### Required for Project\n\nIn addition to the above packages, these must be installed to run the project\nscripts.\n\n- [Spearmint](https://github.com/JasperSnoek/spearmint)\n- [Seaborn](https://seaborn.pydata.org)\n- [NetworkX](https://networkx.github.io)\n- [PyGraphviz](https://pygraphviz.github.io)\n- [PyMOL](https://www.pymol.org)\n\n\u003ca name=\"optional\"\u003e\u003c/a\u003e\n### Optional\n\nSome computationally expensive analyses have built-in acceleration with\n[python_utilities](https://github.com/sdaxen/python_utilities) that activates\nwhen one of the following packages is installed:\n\n- [mpi4py](http://mpi4py.scipy.org)\n- [futures](https://pypi.python.org/pypi/futures)\n\n\u003ca name=\"setup\"\u003e\u003c/a\u003e\n## Setup and Installation\n\nBefore installing, you must manually install [RDKit](http://www.rdkit.org),\nSeaWare, [Spearmint](https://github.com/JasperSnoek/spearmint), and\n[PyMOL](https://www.pymol.org). Additionally you will need `pip` and\n`setuptools`.\n\n### Clone the Repository\n0. Install any of the optional dependencies above.\n1. Download this repository to your machine.\n    - Clone this repository to your machine with\n      `git clone https://github.com/keiserlab/e3fp-paper.git`.\n    - OR download an archive by navigating to\n      [https://github.com/keiserlab/e3fp-paper](https://github.com/keiserlab/e3fp-paper)\n      and clicking \"Download ZIP\". Extract the archive.\n2. Install with\n    ```bash\n    cd e3fp-paper\n    pip install .\n    ```\n\n\u003ca name=\"usage\"\u003e\u003c/a\u003e\n## Usage and Overview\n\nTo use the Python library in a python script, enter: \n```python\nimport e3fp_paper\n```\nSee [`pipeline.py`](e3fp_paper/pipeline.py) for methods for interfacing E3FP's\n[pipeline](https://github.com/keiserlab/e3fp/blob/master/e3fp/pipeline.py)\nwith the specific filetypes used in the paper library.\n\nSee the provided [scripts](project/scripts) for applications of E3FP and of\nthe `e3fp_paper` library.\n\n\u003ca name=\"references\"\u003e\u003c/a\u003e\n## References\n\u003ca name=\"axen2017\"\u003e\u003c/a\u003e\n1. Axen SD, Huang XP, Caceres EL, Gendelev L, Roth BL, Keiser MJ. A Simple\n   Representation Of Three-Dimensional Molecular Structure.\n   *J. Med. Chem.* (2017).\n   doi: [10.1021/acs.jmedchem.7b00696](http://dx.doi.org/10.1021/acs.jmedchem.7b00696). \\\n   \u003ca href=\"http://f1000.com/prime/727824514?bd=1\" target=\"_blank\"\u003e\u003cimg src=\"http://cdn.f1000.com.s3.amazonaws.com/images/badges/badgef1000.gif\" alt=\"Access the recommendation on F1000Prime\" id=\"bg\" /\u003e\u003c/a\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkeiserlab%2Fe3fp-paper","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkeiserlab%2Fe3fp-paper","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkeiserlab%2Fe3fp-paper/lists"}