| Autor: |
Ita K; College of Pharmacy, Touro University, Vallejo, CA, USA., Prinze J; College of Pharmacy, Touro University, Vallejo, CA, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of drug targeting [J Drug Target] 2024 Dec; Vol. 32 (1), pp. 57-65. Date of Electronic Publication: 2024 Jan 12. |
| DOI: |
10.1080/1061186X.2023.2284096 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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