Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

ABSTRACT: Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an in silico screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a prop

SUBMITTER: Ma H 

PROVIDER: S-EPMC9887540 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature