Table 4 Performance of different logP methods against the Martel dataset
From: JPlogP: an improved logP predictor trained using predicted data
Predictor | Performance | % Binned absolute errors | ||||
|---|---|---|---|---|---|---|
RMSE | 0–0.5 | 0.5–1 | 1–1.5 | 1.5–2 | ≥ 2 | |
JPlogP-Coeff | 1.04 | 39.7 | 31.5 | 14.7 | 7.3 | 6.6 |
JPlogP—library | 1.05 | 38.6 | 32.4 | 14.9 | 7.4 | 6.7 |
LogP4Average | 1.12 | 33.9 | 29.4 | 19.2 | 10.8 | 6.6 |
XlogP3-AA | 1.16 | 33.0 | 27.9 | 20.7 | 10.5 | 8.1 |
AAM | 1.18 | 34.2 | 27.6 | 17.5 | 11.7 | 8.9 |
Molinspiration | 1.21 | 32.7 | 26.3 | 20.1 | 11.7 | 9.2 |
SlogP | 1.24 | 32.4 | 27.6 | 19.8 | 10.7 | 9.5 |
ALogP (Vega) | 1.24 | 34.4 | 27.4 | 17.5 | 9.8 | 10.9 |
Biobyte CLogP | 1.24 | 32.0 | 31.3 | 16.8 | 9.1 | 10.9 |
XLogP2 | 1.28 | 31.8 | 24.5 | 19.9 | 11.6 | 12.2 |
AlogPS logP | 1.29 | 25.3 | 28.3 | 20.1 | 16.3 | 10.0 |
ACD | 1.39 | 33.1 | 25.2 | 17.5 | 10.6 | 13.6 |
KowWIN | 1.42 | 32.0 | 23.2 | 18.2 | 11.9 | 14.7 |
Meylan (Vega) | 1.66 | 25.7 | 23.2 | 17.1 | 14.3 | 19.7 |
Mannhold LogP | 1.71 | 14.1 | 16.3 | 23.2 | 20.9 | 25.5 |
MLogP (Vega) | 1.95 | 12.3 | 14.7 | 17.7 | 19.5 | 35.8 |
AlogP (CDK) | 3.72 | 1.6 | 2.5 | 5.0 | 9.1 | 81.9 |