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.
This project helps chemists and computational scientists quickly build and evaluate machine learning models for chemical studies. You provide a CSV file containing molecular descriptors or SMILES strings, and it automatically generates robust models and a publication-quality report detailing the results. It's designed for researchers who need to efficiently apply machine learning to chemical data without extensive programming expertise.
Available on PyPI.
Use this if you are a chemist or computational scientist looking to quickly develop and validate machine learning models from chemical data for your research.
Not ideal if you require deep customization of machine learning algorithms or have highly specialized data formats beyond CSVs of descriptors or SMILES.
Stars
51
Forks
7
Language
Python
License
MIT
Category
Last pushed
Dec 12, 2025
Commits (30d)
0
Dependencies
18
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