Development of a Gaussian Process – feature selection model to characterise (poly)dimethylsiloxane (Silastic®) membrane permeation

Objectives The current study aims to determine the effect of physicochemical descriptor selection on models of polydimethylsiloxane permeation. Methods A total of 2942 descriptors were calculated for a data set of 77 chemicals. Data were processed to remove redundancy, single values, imbalanced and...

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Veröffentlicht in:	Journal of pharmacy and pharmacology 2020-07, Vol.72 (7), p.873-888
Hauptverfasser:	Sun, Yi, Hewitt, Mark, Wilkinson, Simon C., Davey, Neil, Adams, Roderick G., Gullick, Darren R., Moss, Gary P.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms data set design Dimethylpolysiloxanes - chemistry Dimethylpolysiloxanes - pharmacology feature selection Gaussian Process Regression Humans Hydrogen Bonding Learning algorithms Machine Learning Membranes, Artificial Normal Distribution Permeability Polydimethylsiloxane Prediction models Silicones - chemistry Silicones - pharmacology Structure-Activity Relationship
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