  Predicting the interaction between a compound and a target is crucial for
rapid drug repurposing. Deep learning has been successfully applied in
drug-target affinity (DTA) problem. However, previous deep learning-based
methods ignore modeling the direct interactions between drug and protein
residues. This would lead to inaccurate learning of target representation which
may change due to the drug binding effects. In addition, previous DTA methods
learn protein representation solely based on a small number of protein
sequences in DTA datasets while neglecting the use of proteins outside of the
DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel
graph-in-graph neural network with attention mechanism to address the changes
in target representation because of the binding effects. Specifically, a drug
is modeled as a graph of atoms, which then serves as a node in a larger graph
of residues-drug complex. The resulting model is an expressive deep nested
graph neural network. We also use pre-trained protein representation powered by
the recent effort of learning contextualized protein representation. The
experiments are conducted under different settings to evaluate scenarios such
as novel drugs or targets. The results demonstrate the effectiveness of the
pre-trained protein embedding and the advantages our GEFA in modeling the
nested graph for drug-target interaction.
