Aqueous · logP
Partition coefficient logP (cosolvent) · log units
In-sample → out-of-distribution
How the correlation holds when moving to unseen scaffolds.
Vertical marker = the +30% production gate.
Blind calibration slope
Slope of measured vs predicted on the blind set. 1.0 = no magnitude compression.
What it is
Polarity spread drives partitioning. The σ-profile variance — how broadly charge is distributed on the surface — predicts logP with a single inverse-power term.
The physics
A wider σ-profile means more polar surface, which favours the aqueous phase and lowers logP — hence the negative exponent.
How the methods compare
logP is largely solved for interpolation — atom-additive (Wildman–Crippen) and GNN models are cheap and accurate on drug-like space. This kernel's value isn't beating them on RMSE; it's a physically-grounded logP from the very same σ-profile that predicts the other ten properties, with no logP-specific training.
Reported error
Each method's own reported error in log units — lower is better. Different datasets and splits, so this is an orientation, not a controlled benchmark.
Lower error isn’t the whole story — read it against input cost and requirements below.
Input cost
Per-molecule compute/data burden to predict a new molecule — shorter is cheaper.
What each method needs
External dependencies each method carries. An amber dot means the method requires it — fewer dots means fewer things to procure or that can go wrong.
| Method | 3D geometry | MD / sampling | training corpus | a measured value | proprietary params | deps |
|---|---|---|---|---|---|---|
| Aqueous · logP · this kernel | – | – | – | – | 1 | |
| Wildman–Crippen (atom contributions) | – | – | – | – | 1 | |
| clogP (Leo–Hansch fragments) | – | – | – | – | 1 | |
| COSMO-RS / COSMOtherm | – | – | – | 2 | ||
| D-MPNN (Chemprop) | – | – | – | – | 1 |
This kernel needs only a 3D geometry for its one SCF — no MD, no training corpus, no measured value, no proprietary software.
Competing methods
How this property is predicted elsewhere — with the input each method needs (a key differentiator) and the literature reference. Numbers are each method’s own reported figure on its own benchmark, so they are indicative, not a head-to-head on an identical split.
| Method | Class | Reported performance | Input needed | Reference |
|---|---|---|---|---|
| Aqueous · logPthis kernel | closed-form | Pearson r 0.758 (nested-CV) · 0.801 scaffold-blind · MAE 1.01 log units | one DFT SCF σ-profile · no training set · no MD | MF-FQSL (this lab) |
| Wildman–Crippen (atom contributions) | empirical | σ (RMSE) 0.68, r² 0.918 (n=9920) | 2D atom types (68 contributions) | Wildman & Crippen, J. Chem. Inf. Comput. Sci. 1999 |
| clogP (Leo–Hansch fragments) | empirical | ≈0.4–0.5 log on drug-like chemistry (fragment library) | 2D fragment decomposition + correction factors | Leo, Chem. Rev. 1993; BioByte clogP |
| COSMO-RS / COSMOtherm | physics | MAE 0.57, RMSD 0.78 (SAMPL7 blind logP, n=22) | DFT σ-profile + parametrisation | Loschen & Klamt, SAMPL7 logP, J. Comput. Aided Mol. Des. 2021 |
| D-MPNN (Chemprop) | ML / GNN | consensus RMSE ≈ 0.48 log (lipophilicity benchmark) | 2D molecular graph + a labelled training set | Yang et al., J. Chem. Inf. Model. 2019 |
Metrics are as published by each method on its own dataset (different splits, different cohorts) — treat them as an orientation of the landscape, not a controlled benchmark. The differentiator for this kernel is the input column: a single closed-form solve from one σ-profile, with no training corpus, no MD, and no measured melting point.
Descriptors used
Version history
- 2026-05-29v0.91.1 ship
100% atlas coverage. Blind r=0.801, lift +39.3%, slope 1.22.