molecular-machine-learning

Configuration-driven, reproducible ML workflows for molecules and materials, with DOE, auditable artifacts and agent-assisted execution.

Code for the paper: M. Haeberle, P. van Gerwen, R. Laplaza, K. R. Briling, J. Weinreich, F. Eisenbrand, C. Corminboeuf, “Integer linear programming for unsupervised training set selection in molecular machine learning” Mach. Learn.: Sci. Technol. 6 025030 (2025)

Ensemble Deep Learning for Asymmetric Catalysis

Kaggle Bronze Medal solution for NeurIPS Open Polymer Prediction 2025 with molecular descriptors, GNN, CatBoost/XGBoost ensemble.

Quantum Kernel Machine Learning for Drug Design A rigorous, end-to-end Qiskit implementation of quantum kernel SVMs for predicting blood-brain barrier permeability (BBBP) — a core ADMET property in CNS drug discovery — with three controlled experiments that actually test whether the quantum part is doing anything useful.

Graph Neural Networks for Molecular Property Prediction using the QM9 Quantum Chemistry Dataset.

A hands-on tutorial implementing Graph Convolutional Networks (GCNs) for molecular property prediction using PyTorch Geometric. Predicts water solubility from chemical structures with complete pipeline from SMILES to predictions.

Generate the full Tox21 multi-task toxicity dataset from public PubChem BioAssay records

E(3)-equivariant GNNs for protein-ligand binding-affinity prediction (EGNN + e3nn tensor-product), with a machine-precision invariance test suite.