Abstract Chemicals, including some systemically administered xenobiotics and their biotransformations, can be detected noninvasively using skin swabs and untargeted metabolomics analysis. We sought to understand the principal drivers that determine whether a drug taken orally or systemically is likely to be observed on the epidermis by using a random forest classifier to predict which drugs would be detected on the skin. A variety of molecular descriptors describing calculated properties of drugs, such as measures of volume, electronegativity, bond energy, and electrotopology, were used to train the classifier. The mean area under the ROC curve was 0.71 for predicting drug detection on the epidermis, and the SHapley Additive exPlanations model interpretation technique was used to determine the most relevant molecular descriptors. Based on the analysis of 2,561 FDA approved drugs, we predict that therapeutic drug classes such as nervous system drugs are more likely to be detected on the skin. Detecting drugs and other chemicals noninvasively on the skin using untargeted metabolomics could be a useful clinical advancement in therapeutic drug monitoring, adherence, and health status.
