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Peter C. von der Ohe

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#graph neural networks Dataset Open access Oct 2026

NORMAN WS model v0.1: QMRF, model card and SusDat predictions

A regression model for the solubility of organic chemicals in pure water at 15 to 30 °C (log10 mol/L): the mean of gradient-boosted trees (XGBoost and LightGBM, five seeds each) and a message-passing graph neural network (Chemprop D-MPNN, five repeats, with the melting point as an auxiliary task). It was trained on 12...

Peter C. von der Ohe · 0 citations
#graph neural networks Dataset Open access Oct 2026

NORMAN Kow model v0.1: QMRF, model card and SusDat predictions

Regression model for the n-octanol/water partition coefficient (log Kow) of the neutral form of organic chemicals, developed for the NORMAN network: the mean of gradient-boosted trees (XGBoost and LightGBM) and a graph neural network (Chemprop D-MPNN). It was trained on 14 836 structures with measured values, 13 530 fr...

Peter C. von der Ohe · 0 citations
#graph neural networks Dataset Open access Oct 2026

NORMAN aquatic ecotoxicity model v0.1: QMRF, QPRF example, model card and SusDat predictions

A multi-species model for the acute and chronic aquatic toxicity of organic chemicals, developed for the NORMAN network. It predicts toxicity values (log10 µg/L) for 121 species-and-endpoint combinations (heads), of which 82 are released for use (59 acute: LC50/EC50; 23 chronic: NOEC/EC10), covering fish, crustaceans,...

Peter C. von der Ohe · 0 citations

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