kawow 0.1.2

Models for partitioning coefficients (logKow, logKoa and logKaw) from molecular structure, including model based on [Naef & Acree 2024](https://doi.org/10.3390/liquids4010011)

Kawow (under development)

Group-additivity prediction of logKow, logKoa, and logKaw from molecular structure.

Kawow implements some models to predict partitioning coefficients (logKoa, logKow and logKaw), in particular the Naef & Acree (2024) group-additivity scheme using RDKit SMARTS pattern matching. Two model families are available depending on how much transparency or accuracy is required.

Flagging criteria used in outputs

run_models(...) returns B/vB and M/vM flags derived from predicted partition values:

• B: logKoa >= 6 and logKow >= 2
• vB: logKoa >= 6 and logKow >= 5

based on doi:10.1126/science.1138275.

Mobility is computed via an estimated sorption relation:

• logKoc_est = logKow - 0.4
• M: logKoc_est <= 4.5
• vM: logKoc_est <= 3.5

following UBA drinking-water source protection guidance:

• https://www.umweltbundesamt.de/en/publikationen/protecting-the-sources-of-our-drinking-water-the

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Installation

pip install kawow

Or from source (requires RDKit ≥ 2022.9):

git clone https://github.com/LucMiaz/kawow.git
cd kawow
pip install -e ".[dev]"

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Models at a glance

Model key	Class	Approach	logKow R²	logKoa R²
kawow	PartitionCalculator	Ridge regression on Crippen + Naef special-group features	0.922 (cv)	0.946 (cv)
smarts	NaefAcreePartitionCalculator	Pure Naef & Acree 2024 group-additivity (no refitting)	0.857 (S01)	0.785 (S02)
smarts_mixed	NaefAcreeCrippenMixedPartitionCalculator	Naef & Acree additivity + Crippen Ridge hybrid	0.962 (cv)	0.968 (cv)
mqg	MQGPartitionCalculator	Random forest on Molecular Quantum Graph fingerprints	0.881 (cv)	0.945 (cv)

Use run_models() to run several models at once and get per-molecule B/vB and M/vM flags:

import kawow

results = kawow.run_models(
    ["CCCCO", "c1ccccc1", "OC(=O)c1ccccc1"],
    models=["kawow", "smarts_mixed"],
)
for row in results:
    print(row["smiles"], row["models"]["kawow"]["logKow"],
          row["models"]["kawow"]["b_class"])

Each element of the returned list is a dict with:

Key	Description
smiles	canonical SMILES
name	molecule name from input
models	dict keyed by model name; each value contains logKow, logKoa, logKaw, b_class, m_class, ok
ok	True if at least one model succeeded

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1 — PartitionCalculator (recommended for most uses)

Ridge regression fitted on the same S01/S02 datasets. Coefficients are stored in kawow/data/*.json so no re-fitting is needed at import time.

from kawow import PartitionCalculator

calc = PartitionCalculator()           # Ridge (default)

# Single molecule from SMILES
result = calc.predict("CCCCO")        # 1-butanol
print(result)
# {'logKow': 0.88, 'logKoa': 4.12, 'logKaw': -3.24, 'status': 'ok'}

# Batch prediction
smiles = ["c1ccccc1", "CCCCCCCCCC", "OC(=O)c1ccccc1"]
for r in calc.predict_batch(smiles):
    print(r["smiles"], r["logKow"], r["logKoa"], r["logKaw"])

Predict from an InChI string or SDF file:

r = calc.predict("InChI=1S/C4H10O/c1-2-3-4-5/h5H,2-4H2,1H3")
results = calc.predict("compounds.sdf")   # returns list[dict]

Inspect model metadata:

info = calc.model_info
print(info["logKow"])
# {'target': 'logKow', 'n_train': 3234, 'alpha': 51.8,
#  'r2_cv': 0.9221, 'rmse_cv': 0.5775, ...}

Re-fit on your own training data:

import kawow
kawow.fit(
    sdf_logkow="my_logkow.sdf",
    sdf_logkoa="my_logkoa.sdf",
    logkow_prop="logP",
    logkoa_prop="logKoa",
)
calc = kawow.PartitionCalculator()   # reload after fitting

Performance (5-fold cross-validation on Naef & Acree training sets)

Model	Property	n	R² (cv)	RMSE (cv)
kawow (Ridge)	logKow	3 234	0.922	0.578
kawow (Ridge)	logKoa	1 886	0.946	0.660
smarts_mixed (hybrid)	logKow	3 234	0.962	0.403
smarts_mixed (hybrid)	logKoa	1 886	0.968	0.532

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2 — NaefAcreePartitionCalculator (SMARTS additivity, full transparency)

Implements the Naef & Acree 2024 method exactly: each SMARTS pattern from the paper's supplementary tables is matched against the molecule and its tabulated contribution added. No matrix regression — every contribution is directly interpretable.

from kawow.smarts_model import NaefAcreePartitionCalculator

calc = NaefAcreePartitionCalculator(smiles="c1ccccc1")
result = calc.predict("c1ccccc1")
# {'logKow': 2.13, 'logKoa': 2.80, 'logKaw': -0.67, 'in_coverage': True}

# Or pass a pre-built RDKit mol:
from rdkit import Chem
mol = Chem.MolFromSmiles("CCCCCCCCCC")
result = calc.predict(mol)

# Batch via constructor:
calc_batch = NaefAcreePartitionCalculator(
    smiles=["c1ccccc1", "CCCCCCCCCC", "OC(=O)c1ccccc1"]
)
for mol, coeffs in calc_batch.results.items():
    print(coeffs)

Performance on the Naef & Acree training sets

Dataset	Property	n	R²	RMSE	MAE
S01 (Naef 2024)	logKow	3 344	0.857	0.786	0.543
S02 (Naef 2024)	logKoa	1 983	0.785	1.387	0.784
Arp & Hale 2023 (SI)	logKow	687	0.644	1.138	0.686

The remaining error is concentrated in specific chemotypes (notably highly heteroatom-rich agrochemical scaffolds), while the broad SMARTS generalization and pi-environment fixes substantially improved overall logKoa performance on S02.

Correlation plots

logKow vs Naef S01	logKoa vs Naef S02	logKow vs Arp & Hale

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Feature engineering

Each molecule is represented by counts of SMARTS atom-type groups from the Naef & Acree parameter tables, plus five special-group descriptors:

• pi-neighbour moieties — the number of conjugated systems adjacent to a centre atom (controls which entry in a pi-stratified table applies; computed by count_conjugated_neighbor_moieties)
• H-acceptor binary presence — 1 if any intramolecular H-bond donor/acceptor pair is within 5 bonds
• Alkane flag — 1 if the molecule is a pure saturated hydrocarbon
• Unsaturated HC flag — 1 if the molecule is a pure unsaturated hydrocarbon
• Extra −COOH count — number of carboxylic acid groups beyond the first
• Endocyclic C−C single bond count

The PartitionCalculator additionally uses 72 Crippen atom-type features (from RDKit's Crippen.txt) on top of the 5 Naef special groups.

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Reference

Naef, Rudolf, and William E. Acree, Jr. 2024. "Calculation of the Three Partition Coefficients logPow, logKoa and logKaw of Organic Molecules at Standard Conditions at Once by Means of a Generally Applicable Group-Additivity Method." Liquids 4, no. 1: 231–260. 10.3390/liquids4010011

Arp, H.P.H. and Hale, S.E. 2023. "From Measured Partition Coefficients to the Prediction of Environmental Fate." Supplementary data: vg2c00024_si_001 (ACS).

License

MIT