Ensemble Deep Learning for Asymmetric Catalysis
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)
Deep learning on molecules: Minimalistic and powerful implementations of molecular graphs and graph neural networks.
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.
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