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 exploring the intersection of molecular representations, cheminformatics, and machine learning within the field of chemistry. Each section is supplemented with case studies presented as custom JupyterLab exercises, designed to facilitate hands-on learning and the practical application of the concepts discussed.
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