Collection of papers for Molecular Representation using AI

Python snippets for PyMOL to be run in Jupyterlab via the jupyterlab-snippets-multimenus extension.

Amons-based quantum machine learning for quantum chemistry

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."

This project presents a Graph Neural Network (GNN)-based framework for predicting Drug-Drug Interactions (DDIs) using pretrained SMILES embeddings and Graph Attention Networks (GAT). Given the limitations of clinical studies and traditional computational methods in detecting complex DDIs.

Data and codes used in Boy et al. (2025) - Quantum molecular structure encoding

[TMLR 25'] Official implementation of “Self-Supervised Learning on Molecular Graphs: A Systematic Investigation of Masking Design.”

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.