qm9

A short and easy PyTorch implementation of E(n) Equivariant Graph Neural Networks

A Python package for data-mining the QM9 dataset

Predicting properties of small molecules using MPNN on QM9 dataset

A function-centric graph neural network approach for predicting electron densities.

Advanced Guidance Strategies for Conditional Molecule Generation with Flow Matching

Graph Neural Network creation module, implemented in Tensorflow 2 with examples using the module and the iGNNition library for fast GNN prototyping.

A Variational Autoencoder in Google Colab to generate and visualize novel molecular structures for potential drug discovery applications, using the QM9 dataset and SMILES representation.

A from-scratch Message Passing Neural Network (MPNN) for molecular property prediction, built and benchmarked on QM9-style data. Classical AI/DL baseline for a quantum-enhanced drug discovery research project.

A update QM9 benchmark, update form the recent articles

Raman Spectrum Predictor: A machine learning model trained on QM9-like molecules to estimate Raman peak positions near 1600 cm⁻¹. Includes training pipeline, Streamlit UI, and RDKit-based featurization.

A graph neural network project for predicting molecular HOMO-LUMO gap values using QM9-style molecular graph data.

Topology-aware EGNNs with persistent homology features for HOMO–LUMO gap prediction on QM9

SO(3)-эквивариантное предсказание дипольного вектора и тензора поляризуемости молекул (QM9S). SchNet + физические головы ранга 1 и 2, верификация эквивариантности, абляция TDA-признаков с объяснением через теорию представлений.

Master's Thesis: Constrained Molecular Graph Generation with Diffusion Models

Does persistent homology help an E(3)-equivariant model predict molecular dipole moments and polarizability tensors? A controlled study with matched negative controls, whose answer in this configuration is no.

Hybrid quantum-classical graph neural network for molecular property prediction on QM9, built with PennyLane, PyTorch Geometric, and RDKit.