Octanol/Air Partition CoefficientâA General-Purpose Fragment Model to Predict Logâ¯Koa from Molecular Structure.
Environ Sci Technol
; 57(2): 976-984, 2023 01 17.
Article
en En
| MEDLINE
| ID: mdl-36584390
ABSTRACT
The octanol/air partition coefficient Koa is important for assessing the bioconcentration of airborne xenobiotics in foliage and in air-breathing organisms. Moreover, Koa informs about compound partitioning to aerosols and indoor dust, and complements the octanol/water partition coefficient Kow and the air/water partition coefficient Kaw for multimedia fate modeling. Experimental logâ¯Koa at 25 °C has been collected from literature for 2161 compounds with molecular weights from 16 to 959 Da. The curated data set covers 18.2 log units (from -1.0 to 17.2). A newly developed fragment model for predicting logâ¯Koa from molecular structure outperforms COSMOtherm, EPI-Suite KOAWIN, OPERA, and linear solvation energy relationships (LSERs) regarding the root-mean-squared error (rms) and the maximum negative and positive errors (mne and mpe) (rms 0.57 vs 0.86 vs 1.09 vs 1.19 vs 1.05-1.53, mne -2.55 vs -3.95 vs -7.51 vs -7.54 vs (-5.63) - (-7.34), mpe 2.91 vs 5.97 vs 7.54 vs 4.24 vs 6.89-10.2 log units). The prediction capability, statistical robustness, and sound mechanistic basis are demonstrated through initial separation into a training and prediction set (8020%), mutual leave-50%-out validation, and target value scrambling in terms of temporarily wrong compound-Koa allocations. The new general-purpose model is implemented in a fully automatized form in the ChemProp software available to the public. Regarding Koa indirectly determined through Kow and Kaw, a new approach is developed to convert from wet to dry octanol, enabling higher consistency in experimental (and thus also predicted) Koa.
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1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Agua
/
Modelos Químicos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Environ Sci Technol
Año:
2023
Tipo del documento:
Article
País de afiliación:
Alemania