Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction

Oral bioavailability is a pharmacokinetic property that
plays an
important role in drug discovery. Recently developed computational
models involve the use of molecular descriptors, fingerprints, and
conventional machine-learning models. However, determining the type
of molecular descriptors requires domain expert knowledge and time
for feature selection. With the emergence of the graph neural network
(GNN), models can be trained to automatically extract features that
they deem important. In this article, we exploited the automatic feature
selection of GNN to predict oral bioavailability. To enhance the prediction
performance of GNN, we utilized transfer learning by pre-training
a model to predict solubility and obtained a final average accuracy
of 0.797, an F1 score of 0.840, and an AUC-ROC of 0.867, which outperformed
previous studies on predicting oral bioavailability with the same
test data set.