Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks.

Abstract

Graph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture richer protein and drug features, leading to improved DTA prediction performance. However, existing methods often neglect to incorporate valuable protein cavity information, a key aspect of protein physical chemistry. This study addresses this gap by proposing a novel topology-enhanced GNN for DTA prediction that integrates protein pocket data. Additionally, we optimize training and message-passing strategies to enhance the model's feature representation capabilities. Our model's effectiveness is validated on the Davis and KIBA data sets, demonstrating its ability to capture the intricate interplay between drugs and targets. The source code is publicly available on https://github.com/ZZDXgangwu/DTA.