Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge

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 solu...

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Bibliographic Details
Published in:BMC chemistry 2015-09, Vol.9 (1), p.50-7, Article 50
Main Authors: Buonaiuto, Michael A., Lang, Andrew S. I. D.
Format: Article
Language:English
Subjects:
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Environmental chemistry
Forests
Jean-Claude Bradley Memorial Series
Mathematical models
Melting points
Permissible error
Pharmacology
Research Article
Solubility
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Summary: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) R 2 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. Graphical abstract :
ISSN:1752-153X
1752-153X
2661-801X
DOI:10.1186/s13065-015-0131-2