{"id":15671596,"url":"https://github.com/jvalegre/robert","last_synced_at":"2025-04-13T04:23:43.523Z","repository":{"id":43930473,"uuid":"450838865","full_name":"jvalegre/robert","owner":"jvalegre","description":"Automated machine learning protocols that start from CSV databases of descriptors or SMILES and produce publication-quality results in Chemistry studies with only one command line.","archived":false,"fork":false,"pushed_at":"2025-04-09T14:12:07.000Z","size":106833,"stargazers_count":46,"open_issues_count":1,"forks_count":6,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-09T15:26:19.479Z","etag":null,"topics":["automation","cheminformatics","machine-learning","python","reproducibility","scikit-learn","workflows"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jvalegre.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":"2022-01-22T14:22:41.000Z","updated_at":"2025-04-09T13:34:42.000Z","dependencies_parsed_at":"2023-01-30T19:00:22.706Z","dependency_job_id":"419a26fc-5875-4be2-9c09-845caf5a5d18","html_url":"https://github.com/jvalegre/robert","commit_stats":{"total_commits":273,"total_committers":4,"mean_commits":68.25,"dds":"0.35531135531135527","last_synced_commit":"203fe5721c620273d2dcbcc31ef6e2cddb1b98d2"},"previous_names":[],"tags_count":10,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvalegre%2Frobert","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvalegre%2Frobert/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvalegre%2Frobert/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvalegre%2Frobert/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jvalegre","download_url":"https://codeload.github.com/jvalegre/robert/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248662340,"owners_count":21141561,"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":["automation","cheminformatics","machine-learning","python","reproducibility","scikit-learn","workflows"],"created_at":"2024-10-03T15:03:54.941Z","updated_at":"2025-04-13T04:23:43.496Z","avatar_url":"https://github.com/jvalegre.png","language":"Python","funding_links":[],"categories":["Machine Learning"],"sub_categories":[],"readme":"![](Logos/Robert_logo.jpg)\n#\n## \u003cp align=\"center\"\u003e ROBERT (Refiner and Optimizer of a Bunch of Existing Regression Tools)\u003c/p\u003e\n\n\n[![CircleCI](https://img.shields.io/circleci/build/github/jvalegre/robert?label=Circle%20CI\u0026logo=circleci)](https://app.circleci.com/pipelines/github/jvalegre/robert)\n[![Codecov](https://img.shields.io/codecov/c/github/jvalegre/robert?label=Codecov\u0026logo=codecov)](https://codecov.io/gh/jvalegre/robert)\n[![Downloads](https://img.shields.io/pepy/dt/robert?label=Downloads\u0026logo=pypi)](https://www.pepy.tech/projects/robert)\n[![Read the Docs](https://img.shields.io/readthedocs/robert?label=Read%20the%20Docs\u0026logo=readthedocs)](https://robert.readthedocs.io/)\n[![PyPI](https://img.shields.io/pypi/v/robert)](https://pypi.org/project/robert/)\n\n## Documentation  \nFull documentation with installation instructions, technical details and examples can be found in [Read the Docs](https://robert.readthedocs.io).  \n\nDon't miss out the latest hands-on tutorials from our [YouTube channel](https://www.youtube.com/channel/UCHRqI8N61bYxWV9BjbUI4Xw)!\n\n## Recommended installation\n1. (Only once) Create new conda environment: `conda create -n robert python=3.10`  \n2. Activate conda environment: `conda activate robert`  \n3. Install ROBERT using pip: `pip install robert` \n4. Install libraries for the PDF report `conda install -y -c conda-forge glib gtk3 pango mscorefonts`\n5. (Only for compatible devices) Install Intelex accelerator: `pip install scikit-learn-intelex==2025.0.1`  \n* Inexperienced users should visit the *Users with no Python experience* section in [Read the Docs](https://robert.readthedocs.io).\n## Update the program\n1. Update to the latest version: `pip install robert --upgrade`  \n\n## Developers and help desk  \nList of main developers and contact emails:  \n  - [ ] [Juan V. Alegre-Requena](https://orcid.org/0000-0002-0769-7168). Contact: [jv.alegre@csic.es](mailto:jv.alegre@csic.es)  \n  - [ ] [David Dalmau Ginesta](https://orcid.org/0000-0002-2506-6546). Contact: [ddalmau@unizar.es](mailto:ddalmau@unizar.es)  \n\nFor suggestions and improvements of the code (greatly appreciated!), please reach out through the issues and pull requests options of Github.  \n\n## License\nROBERT is freely available under an [MIT](https://opensource.org/licenses/MIT) License  \n\n## Special acknowledgements\nJ.V.A.R. - The acronym ROBERT is dedicated to **ROBERT Paton**, who was a mentor to me throughout my years at Colorado State University and who introduced me to the field of cheminformatics. Cheers mate!\n\nD.D.G. - The style of the ROBERT_report.pdf file was created with the help of **Oliver Lee** (2023, Zysman-Colman group at University of St Andrews).\n\nJ.V.A.R. and D.D.G. - The improvements from v1.0 to v1.2 are largely the result of insightful discussions with **Matthew Sigman** and his students, **Jamie Cadge** and **Simone Gallarati** (2024, University of Utah).\n\nWe really THANK all the testers for their feedback and for participating in the reproducibility tests, including:\n\n* **David Valiente** (2022-2023, Universidad Miguel Hernández)\n* **Heidi Klem** (2023, Paton group at Colorado State University)\n* **Iñigo Iribarren** (2023, Trujillo group at Trinity College Dublin)\n* **Guilian Luchini** (2023, Paton group at Colorado State University)\n* **Alex Platt** (2023, Paton group at Colorado State University)\n* **Oliver Lee** (2023, Zysman-Colman group at University of St Andrews)\n* **Xinchun Ran** (2023, Yang group at Vanderbilt University)\n\n## How to cite ROBERT\nIf you use any of the ROBERT modules, please include this citation:  \n* Dalmau, D.; Alegre Requena, J. V. ROBERT: Bridging the Gap between Machine Learning and Chemistry. *Wiley Interdiscip. Rev. Comput. Mol. Sci.* **2024**, *14*, e1733.\n\nIf you use the AQME module, please include this citation:  \n* Alegre-Requena et al., AQME: Automated Quantum Mechanical Environments for Researchers and Educators. *Wiley Interdiscip. Rev. Comput. Mol. Sci.* **2023**, *13*, e1663.\n\nAdditionally, please include the corresponding reference for Scikit-learn and SHAP:  \n* Pedregosa et al., Scikit-learn: Machine Learning in Python. *J. Mach. Learn. Res.* **2011**, *12*, 2825-2830.  \n* Lundberg et al., From local explanations to global understanding with explainable AI for trees. *Nat. Mach. Intell.* **2020**, *2*, 56–67.  ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjvalegre%2Frobert","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjvalegre%2Frobert","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjvalegre%2Frobert/lists"}