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

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 property augmentation
strategy to include massive training data with other property information.
Extensive experiments demonstrate that our proposed method significantly
outperforms all existing baselines on the DILI data set by obtaining
a 81.4% accuracy using cross-validation with random splitting, 78.7%
using leave-one-out cross-validation, and 76.5% using cross-validation
with scaffold splitting.