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Background: 1-Octanol solubility is important in a variety of applications involving pharmacology and environmental chemistry. Current models are linear in nature and often require foreknowledge of either melting point or aqueous solubility. Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure. Results: We created a random forest model using CDK descriptors that has an out-of-bag (OOB) R2 value of 0.66 and an OOB mean squared error of 0.34. The model has been deployed for general use as a Shiny application. Conclusion: The 1-octanol solubility model provides reasonably accurate predictions of the 1-octanol solubility of organic solutes directly from structure. The model was developed under Open Notebook Science conditions which makes it open, reproducible, and as useful as possible


Article in Chemistry Central Journal

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