{"id":15661021,"url":"https://github.com/rasbt/predicting-activity-by-machine-learning","last_synced_at":"2026-03-04T08:31:45.793Z","repository":{"id":66172064,"uuid":"95918011","full_name":"rasbt/predicting-activity-by-machine-learning","owner":"rasbt","description":"Activity From Virtual Screening Code Repository","archived":false,"fork":false,"pushed_at":"2017-08-30T20:33:46.000Z","size":2369,"stargazers_count":23,"open_issues_count":1,"forks_count":17,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-05-05T21:16:29.116Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/rasbt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"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":"2017-06-30T19:29:10.000Z","updated_at":"2025-04-26T12:22:30.000Z","dependencies_parsed_at":"2023-06-25T22:31:36.339Z","dependency_job_id":null,"html_url":"https://github.com/rasbt/predicting-activity-by-machine-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rasbt/predicting-activity-by-machine-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpredicting-activity-by-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpredicting-activity-by-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpredicting-activity-by-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpredicting-activity-by-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rasbt","download_url":"https://codeload.github.com/rasbt/predicting-activity-by-machine-learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fpredicting-activity-by-machine-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30076857,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-04T08:01:56.766Z","status":"ssl_error","status_checked_at":"2026-03-04T08:00:42.919Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":[],"created_at":"2024-10-03T13:25:24.705Z","updated_at":"2026-03-04T08:31:45.748Z","avatar_url":"https://github.com/rasbt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# \"Automated Inference of Chemical Discriminants of Biological Activity\" Code Repository\n\n\nComplimentary dataset and code for the chapter\n\n**\"Automated Inference of Chemical Discriminants of Biological Activity\"**\n\nin the book *\"Computational drug discovery and design\"** (Methods in Molecular Biology, Springer Protocols).\n\nAuthors:  \n[Sebastian Raschka](https://sebastianraschka.com), [Anne M. Scott](https://msu.edu/~liweim/index.htm), [Mar Huertas](http://www.bio.txstate.edu/about/Faculty---Staff/faculty/Huertas--Mar.html), [Weiming Li](https://msu.edu/~liweim/index.htm), and [Leslie A. Kuhn](http://www.kuhnlab.bmb.msu.edu)\n\n\n---\n---\n\n\n## Requirements\n\n#### Python Interpreter\n\nTo run the code examples, a recent version of Python is required (3.5 or \nnewer) is required; Python 3.6 is recommended. \nYou can https://www.python.org/downloads/. \n\n#### Python Libraries\n\nThe following list specifies the Python libraries used in this chapter, the recommended version number, and a short description of their use:\n-   NumPy version 1.13.0 or newer (http://www.numpy.org); numerical array library\n-   SciPy version 0.19.0 or newer (https://www.scipy.org); advanced functions for scientific computing\n-   Pandas version 0.20.1 or newer (http://pandas.pydata.org); handling of CSV files and working with data frames\n-   Matplotlib version 2.0.2 or newer (https://matplotlib.org); 2D plotting \n-   Scikit-learn version 0.18.1 or newer (http://scikit-learn.org/stable/); algorithms for machine learning \n-   MLxtend version 0.7.0 or newer (http://rasbt.github.io/mlxtend/); sequential feature selection algorithms\n\nThe scientific computing libraries listed above can be installed using Python's in-built [Pip](https://pypi.python.org/pypi/pip) module  by executing the following line of code directly from a macOS/Unix, Linux, or Windows MS-DOS terminal command line:\n\n    pip install numpy scipy pandas matplotlib scikit-learn pydotplus mlxtend\n\nIf you encounter problems with version incompatibilities, you can specify the package versions explicitly, as shown in the following terminal command example:\n\n    pip install numpy==1.13.0 scipy==0.19.0 pandas==0.20.1 matplotlib==2.0.2 scikit-learn==0.18.1 pydotplus==2.0.2 mlxtend==0.7.0\n\n#### Graph Visualization Software\n\nTo visualize the decision trees later in this chapter, an installation of GraphViz is needed. The GraphViz downloader is freely available at http://www.graphviz.org with the installation and setup instructions.\n\n\n#### Jupyter Notebook\n\nTo open and execute the code in the file [code/dkpes_fgroup_analysis.ipynb](code/dkpes_fgroup_analysis.ipynb) locally, Jupyter Notebook for Python is required. For more information on installing Jupyter Notebook, please visit http://jupyter.readthedocs.io/en/latest/install.html.\n\nAlternatively, if you don't want to install Jupyter Notebook, you can view the code in your browser by clicking on the [code/dkpes_fgroup_analysis.ipynb](code/dkpes_fgroup_analysis.ipynb) file in this GitHub repository.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fpredicting-activity-by-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frasbt%2Fpredicting-activity-by-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fpredicting-activity-by-machine-learning/lists"}