Azetidines represent one of the most important four-membered heterocycles used in organic synthesis and medicinal chemistry. The reactivity of azetidines is driven by a considerable ring strain, while at the...
An early hurdle in the optimization
of small-molecule chemical
probes and drug discovery entities is the attainment of sufficient
exposure in the mouse via oral administration of the compound. While
computational approaches have attempted to predict molecular properties
related to the mouse pharmacokinetic (PK) profile, we present herein
a machine learning approach to specifically predict the oral exposure
of a compound as measured in the mouse snapshot PK assay. A random
forest workflow was found to produce the best cross-validation and
external test set statistics after processing of the input data set
and optimization of model features. The modeling approach should be
useful to the chemical biology and drug discovery communities to predict
this key molecular property and afford chemical entities of translational
significance.
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