  The characterization of drug-protein interactions is crucial in the
high-throughput screening for drug discovery. The deep learning-based
approaches have attracted attention because they can predict drug-protein
interactions without trial-and-error by humans. However, because data labeling
requires significant resources, the available protein data size is relatively
small, which consequently decreases model performance. Here we propose two
methods to construct a deep learning framework that exhibits superior
performance with a small labeled dataset. At first, we use transfer learning in
encoding protein sequences with a pretrained model, which trains general
sequence representations in an unsupervised manner. Second, we use a Bayesian
neural network to make a robust model by estimating the data uncertainty. As a
result, our model performs better than the previous baselines for predicting
drug-protein interactions. We also show that the quantified uncertainty from
the Bayesian inference is related to the confidence and can be used for
screening DPI data points.
