  Drug-induced liver injury (DILI) is the most common cause of acute liver
failure and a frequent reason for withdrawal of candidate drugs during
preclinical and clinical testing. An important type of DILI is cholestatic
liver injury, caused by buildup of bile salts within hepatocytes; it is
frequently associated with inhibition of bile salt transporters, such as the
bile salt export pump (BSEP). Reliable in silico models to predict BSEP
inhibition directly from chemical structures would significantly reduce costs
during drug discovery and could help avoid injury to patients. Unfortunately,
models published to date have been insufficiently accurate to encourage wide
adoption. We report our development of classification and regression models for
BSEP inhibition with substantially improved performance over previously
published models. Our model development leveraged the ATOM Modeling PipeLine
(AMPL) developed by the ATOM Consortium, which enabled us to train and evaluate
thousands of candidate models. In the course of model development, we assessed
a variety of schemes for chemical featurization, dataset partitioning and class
labeling, and identified those producing models that generalized best to novel
chemical entities. Our best performing classification model was a neural
network with ROC AUC = 0.88 on our internal test dataset and 0.89 on an
independent external compound set. Our best regression model, the first ever
reported for predicting BSEP IC50s, yielded a test set $R^2 = 0.56$ and mean
absolute error 0.37, corresponding to a mean 2.3-fold error in predicted IC50s,
comparable to experimental variation. These models will thus be useful as
inputs to mechanistic predictions of DILI and as part of computational
pipelines for drug discovery.
