GEFA: Early Fusion Approach in Drug-Target Affinity Prediction

by   Tri Minh Nguyen, et al.

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.


page 9

page 10

page 12


DeepDTA: Deep Drug-Target Binding Affinity Prediction

The identification of novel drug-target (DT) interactions is a substanti...

Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity

Modeling the effects of mutations on the binding affinity plays a crucia...

Drug-Target Interaction Prediction with Graph Attention networks

Motivation: Predicting Drug-Target Interaction (DTI) is a well-studied t...

One-shot screening of potential peptide ligands on HR1 domain in COVID-19 glycosylated spike (S) protein with deep siamese network

The novel coronavirus (2019-nCoV) has been declared to be a new internat...

Bayes Optimal Informer Sets for Early-Stage Drug Discovery

An important experimental design problem in early-stage drug discovery i...

GDGRU-DTA: Predicting Drug-Target Binding Affinity Based on GNN and Double GRU

The work for predicting drug and target affinity(DTA) is crucial for dru...