Machine learning for skin permeability prediction: random forest and XG boost regression

Abstract

Background: Machine learning algorithms that can quickly and easily estimate skin permeability (Kp) are increasingly being used in drug delivery research. The linear free energy relationship (LFER) developed by Abraham is a practical technique for predicting Kp. The permeability coefficients and Abraham solute descriptor values for 175 organic compounds have been documented in the scientific literature.

Purpose: The purpose of this project was to use a publicly available dataset to make skin permeability predictions using the random forest and XBoost regression techniques.

Methods: We employed Pandas-based methods in JupyterLab to predict permeability coefficient (Kp) from solute descriptors (excess molar refraction [E], combined dipolarity/polarizability [S], overall solute hydrogen bond acidity and basicity [A and B], and the McGowan's characteristic molecular volume [V]).

Results: The random forest and XG Boost regression models established statistically significant association between the descriptors and the skin permeability coefficient.

Keywords:

• Skin permeability
• random forest
• transdermal
• pandas
• descriptors

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.