Abstract
Background1-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.ResultsWe 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.ConclusionThe 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.Graphical abstract:Electronic supplementary materialThe online version of this article (doi:10.1186/s13065-015-0131-2) contains supplementary material, which is available to authorized users.
Highlights
The solubility of organic compounds in 1-octanol is important because of its direct relationship to the partition coefficient logP used in pharmacology and environmental chemistry
We removed all items that were marked “DONOTUSE.” For compounds with multiple solubility values that included values listed in the Abraham and Acree paper, we kept only the solubility values that were listed in the Abraham and Acree paper
Modeling A Random Forest Model is a compilation of uncorrelated decision trees used to choose the best case among many
Summary
The solubility of organic compounds in 1-octanol is important because of its direct relationship to the partition coefficient logP used in pharmacology and environmental chemistry. Current models that can be used to predict 1-octanol solubility include group contribution methods [1] and often include melting point as a descriptor [2,3,4]. The most recent model by Admire and Yalkowsky [4] gives a very useful rule of thumb to predict molar 1-octanol solubility from just the melting point. Abraham and Acree [5] refined Admire and Yalkowsky’s model by appending the melting point term to their linear free energy relationship (LFER) model. The coefficients were found using linear regression against the solubilities of solutes with known Abraham descriptors with the following result: Log Soct = 0.480 − 0.355 · E − 0.203 · S + 1.521 · A − 0.408 · B + 0.364 · V − 1.294 · A · B − 0.00813 · (mp − 25)
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