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)
A python package for building powerful molecular graph neural networks for chemistry and biology.
Kaggle Bronze Medal solution for NeurIPS Open Polymer Prediction 2025 with molecular descriptors, GNN, CatBoost/XGBoost ensemble.
Ensemble Deep Learning for Asymmetric Catalysis
Graph Neural Networks for Molecular Property Prediction using the QM9 Quantum Chemistry Dataset.
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.
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
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