Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints

Figure 1

The workflow of this study. We collected drug features from various databases including DrugBank, LINCS, and WHO’s ATC database and curated DILI labels from DrugDex, DrugPoints, and DailyMed for oral drugs. We split 20% of the dataset as an independent test set and used the remaining 80% for ten-fold cross-validations. We generated or collected the drug features and developed two types of models, logistic regression (LR) and random forest (RF), using different combinations of parameters and used the best parameters for independent tests.