  The protein-ligand binding affinity (PLA) prediction goal is to predict
whether or not the ligand could bind to a protein sequence. Recently, in PLA
prediction, deep learning has received much attention. Two steps are involved
in deep learning-based approaches: feature extraction and task prediction step.
Many deep learning-based approaches concentrate on introducing new feature
extraction networks or integrating auxiliary knowledge like protein-protein
interaction networks or gene ontology knowledge. Then, a task prediction
network is designed simply using some fully connected layers. This paper aims
to integrate retrieved similar hard protein-ligand pairs in PLA prediction
(i.e., task prediction step) using a semi-supervised graph convolutional
network (GCN). Hard protein-ligand pairs are retrieved for each input query
sample based on the manifold smoothness constraint. Then, a graph is learned
automatically in which each node is a protein-ligand pair, and each edge
represents the similarity between pairs. In other words, an end-to-end
framework is proposed that simultaneously retrieves hard similar samples,
learns protein-ligand descriptor, learns the graph topology of the input sample
with retrieved similar hard samples (learn adjacency matrix), and learns a
semi-supervised GCN to predict the binding affinity (as task predictor). The
training step adjusts the parameter values, and in the inference step, the
learned model is fine-tuned for each input sample. To evaluate the proposed
approach, it is applied to the four well-known PDBbind, Davis, KIBA, and
BindingDB datasets. The results show that the proposed method significantly
performs better than the comparable approaches.
