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Natural toxins are ubiquitously occurring highly diverse organic compounds produced by e.g., plants or fungi. In predictive environmental fate and risk assessment of organic chemicals for regulatory purposes, the octanol-water partition coefficient (Kow) remains one of the key parameters. However, experimental data for natural toxins is largely missing and current estimation models for Kow show limited applicability for multifunctional, ionizable compounds. Thus, log Kow data was first experimentally derived for a diverse set of 45 largely ionizable natural toxins and then compared to predicted values from three different models (KOWWIN, ACD/Percepta, Chemicalize). Both approaches were critically evaluated with regards to their applicability for multifunctional, ionizable compounds. The miniaturized shake-flask approach allowed reliable quantification of pH dependent partitioning behavior for neutral, acidic and basic ionizable natural toxins. All analyzed toxins are rather polar with an average log Kow < 1 and an observed maximum log Kow of 2.7. Furthermore, the comparison of experimental data to those of commonly used prediction models showed that the latter match the former with only minorly increased errors. The Chemicalize tool gave overall best predictions with a mean absolute error of 0.49 and thus should be preferred in comparison to ACD/Percepta and KOWWIN.