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https://github.com/jvalegre/robert

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.
https://github.com/jvalegre/robert

automation cheminformatics machine-learning python reproducibility scikit-learn workflows

Last synced: 3 days ago
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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.

Host: GitHub
URL: https://github.com/jvalegre/robert
Owner: jvalegre
License: mit
Created: 2022-01-22T14:22:41.000Z (almost 3 years ago)
Default Branch: master
Last Pushed: 2024-10-29T05:23:46.000Z (7 days ago)
Last Synced: 2024-10-30T09:15:50.292Z (6 days ago)
Topics: automation, cheminformatics, machine-learning, python, reproducibility, scikit-learn, workflows
Language: Python
Homepage:
Size: 101 MB
Stars: 32
Watchers: 3
Forks: 5
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        ![](Logos/Robert_logo.jpg)

#

## 
 ROBERT (Refiner and Optimizer of a Bunch of Existing Regression Tools)


[![CircleCI](https://img.shields.io/circleci/build/github/jvalegre/robert?label=Circle%20CI&logo=circleci)](https://app.circleci.com/pipelines/github/jvalegre/robert)

[![Codecov](https://img.shields.io/codecov/c/github/jvalegre/robert?label=Codecov&logo=codecov)](https://codecov.io/gh/jvalegre/robert)

[![Downloads](https://img.shields.io/pepy/dt/robert?label=Downloads&logo=pypi)](https://www.pepy.tech/projects/robert)

[![Read the Docs](https://img.shields.io/readthedocs/robert?label=Read%20the%20Docs&logo=readthedocs)](https://robert.readthedocs.io/)

[![PyPI](https://img.shields.io/pypi/v/robert)](https://pypi.org/project/robert/)

## Documentation  

Full documentation with installation instructions, technical details and examples can be found in [Read the Docs](https://robert.readthedocs.io).  

Don't miss out the latest hands-on tutorials from our [YouTube channel](https://www.youtube.com/channel/UCHRqI8N61bYxWV9BjbUI4Xw)!

## Recommended installation

1. (Only once) Create new conda environment: `conda create -n robert python=3.10`  

2. Activate conda environment: `conda activate robert`  

3. Install ROBERT using pip: `pip install robert` 

4. Install libraries for the PDF report `conda install -y -c conda-forge glib gtk3 pango mscorefonts`

5. (Only for compatible devices) Install Intelex accelerator: `pip install scikit-learn-intelex`  

* Inexperienced users should visit the *Users with no Python experience* section in [Read the Docs](https://robert.readthedocs.io).

## Update the program

1. Update to the latest version: `pip install robert --upgrade`  

## Developers and help desk  

List of main developers and contact emails:  

  - [ ] [Juan V. Alegre-Requena](https://orcid.org/0000-0002-0769-7168). Contact: [[email protected]](mailto:[email protected])  

  - [ ] [David Dalmau Ginesta](https://orcid.org/0000-0002-2506-6546). Contact: [[email protected]](mailto:[email protected])  

For suggestions and improvements of the code (greatly appreciated!), please reach out through the issues and pull requests options of Github.  

## License

ROBERT is freely available under an [MIT](https://opensource.org/licenses/MIT) License  

## Special acknowledgements

J.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!

D.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).

J.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).

We really THANK all the testers for their feedback and for participating in the reproducibility tests, including:

* **David Valiente** (2022-2023, Universidad Miguel Hernández)

* **Heidi Klem** (2023, Paton group at Colorado State University)

* **Iñigo Iribarren** (2023, Trujillo group at Trinity College Dublin)

* **Guilian Luchini** (2023, Paton group at Colorado State University)

* **Alex Platt** (2023, Paton group at Colorado State University)

* **Oliver Lee** (2023, Zysman-Colman group at University of St Andrews)

* **Xinchun Ran** (2023, Yang group at Vanderbilt University)

## How to cite ROBERT

If you use any of the ROBERT modules, please include this citation:  

* Dalmau, D.; Alegre Requena, J. V. ROBERT: Bridging the Gap between Machine Learning and Chemistry. *Wiley Interdiscip. Rev. Comput. Mol. Sci.* **2024**, *accepted*. DOI: 10.1002/WCMS.1733.  

If you use the AQME module, please include this citation:  

* Alegre-Requena et al., AQME: Automated Quantum Mechanical Environments for Researchers and Educators. *Wiley Interdiscip. Rev. Comput. Mol. Sci.* **2023**, *13*, e1663.

Additionally, please include the corresponding reference for Scikit-learn and SHAP:  

* Pedregosa et al., Scikit-learn: Machine Learning in Python. *J. Mach. Learn. Res.* **2011**, *12*, 2825-2830.  

* Lundberg et al., From local explanations to global understanding with explainable AI for trees. *Nat. Mach. Intell.* **2020**, *2*, 56–67.