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Molecular properties
Lipophilicity (AstraZeneca)
AstraZeneca's lipophilicity dataset measures how compounds distribute between octanol and water at pH 7.4. The benchmark uses molecular fingerprints to predict logD, the logarithm of this distribution coefficient.
RegressionLicence: CC BY 4.0 via TDC; underlying source terms require verification before re-hostingTDC-Lipophilicity-AstraZenecaTarget: Ytdc ↗
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At this operating point
Model comparison
p=10,000, n=100 · Lower RMSE is better
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Random Forest is marked with a diamond. Models are ordered by the selected metric.
Across sample budgets
Learning curves
All metric values at this operating point
| Model | RMSE | MAE | R² |
|---|---|---|---|
| TabPFN 3.5 | 1.163 | 0.9354 | 0.06196 |
| LimiX2 | 1.172 | 0.9345 | 0.04571 |
| Extra Trees | 1.175 | 0.9338 | 0.04187 |
| Random Forest | 1.176 | 0.9345 | 0.04061 |
| RealTabPFN 2.5 | 1.181 | 0.9493 | 0.03327 |
| Random Forest (tuned) | 1.182 | 0.9467 | 0.03086 |
| TabM | 1.185 | 0.9524 | 0.02718 |
| RealTabPFN v2 | 1.186 | 0.9561 | 0.02603 |
| XGBoost | 1.186 | 0.95 | 0.02504 |
| GeneICL | 1.186 | 0.9514 | 0.02423 |
| TabDPT | 1.191 | 0.9459 | 0.01539 |
| MLP (tuned) | 1.196 | 0.9607 | 0.008639 |
| Logistic Regression (tuned) | 1.197 | 0.9634 | 0.0068 |
| MITRA | 1.199 | 0.9706 | 0.003576 |
| XGBoost (tuned) | 1.2 | 0.967 | 0.003094 |
| MLP | 1.209 | 0.9691 | -0.01252 |
| Logistic Regression | 1.213 | 0.9655 | -0.02234 |
| AutoGluon | 1.227 | 0.9649 | -0.04633 |
| Extra Trees (tuned) | 1.253 | 1.002 | -0.109 |
| Kumo Tabular (M) | 1.257 | 0.9859 | -0.09743 |
| TabFM | 1.259 | 0.9887 | -0.1031 |
| RealMLP | 1.268 | 1.005 | -0.1137 |
| Causilo | 1.331 | 1.05 | -0.2294 |