Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).

ABSTRACT: Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank database, which classifies DILI severity and potential. These classifications have been used by various research groups in generating computational predictions for this type of liver injury. Recently, groups from Pfizer and AstraZeneca have collated DILI in vitro data and physicochemical properties for compounds that can be used along with data from the FDA to build machine learning models for DILI. In this study, we have used these data sets, as well as the Biopharmaceutics Drug Disposition Classification System data set, to generate Baye

SUBMITTER: Minerali E 

PROVIDER: S-EPMC7702310 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature