The influence of diffusion cell type and experimental temperature on machine learning models of skin permeability.

Objectives: The aim of this study was to use Gaussian process regression (GPR) methods to quantify the effect of experimental temperature (T ) and choice of diffusion cell on model quality and performance.

Methods: Data were collated from the literature. Static and flow-through diffusion cell data were separated, and a series of GPR experiments was conducted.

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression.

Objectives: The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters.

Methods: Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or 'chemical space' of the key descriptors to assess the effect of the data range on model quality.