Graph Neural Networks for Predicting Drug-Target Interactions with Structural Similarity

Abstract

Predicting drug-target interactions (DTIs) is a crucial task in drug discovery, aiming to identify potential therapeutic candidates and minimize adverse effects. Traditional methods often rely on high-throughput screening or homology modeling, which are time-consuming and expensive. This paper proposes a novel approach utilizing Graph Neural Networks (GNNs) to predict DTIs based on the structural similarity between drugs and their target proteins. We represent both drugs and proteins as graphs, where nodes represent atoms and edges represent bonds, and employ Graph Convolutional Networks (GCNs) to learn embeddings that capture the structural features of these molecules. The learned embeddings are then used to predict the likelihood of interaction between a drug and a target protein. Our results demonstrate that GNNs can achieve competitive accuracy in DTI prediction, offering a computationally efficient and structurally informed alternative to existing methods. The core claim is that GNNs can accurately predict DTIs by leveraging the structural similarity between drugs and their target proteins. The core mechanism involves representing drugs and proteins as graphs and utilizing GCNs to learn embeddings that capture their structural features and predict interactions based on similarity.