Amons-based quantum machine learning for quantum chemistry
Python snippets for PyMOL to be run in Jupyterlab via the jupyterlab-snippets-multimenus extension.
Exploring QSAR Models for Activity-Cliff Prediction
This repository was created to provide code and data to support the article "Matrix of Orthogonalised Atomic Orbital Coefficients Representation for Radicals and Ions."
Tutorial for the generation of the MODA descriptor to predict magnetic exchange couplings. This repository is associated with the manuscript entitled "Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning", by Raul Santiago, Sergi Vela, Mercè Deumal and Jordi Ribas-Arino, from the GEM2
Literature review into molecular representations, cheminformatics and machine learning in chemistry. Each section has accompanying JupyterLab notebooks exercises, as well as Python classes and utility functions.
Add a description, image, and links to the molecular-representation topic page so that developers can more easily learn about it.
To associate your repository with the molecular-representation topic, visit your repo's landing page and select "manage topics."