Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR.

ABSTRACT: The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model's training and validation performanc

SUBMITTER: Hirte S 

PROVIDER: S-EPMC9029776 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature