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

Journal: Journal of chemical information and modeling
PMID: 40159647

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.

Authors

  • Hengliang Guo
    National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou, 450001, China.
  • Congxiang Zhang
    School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
  • Jiandong Shang
    National Supercomputing Center in Zhengzhou, Zhengzhou University, Henan, China.
  • Dujuan Zhang
    National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou, 450001, China.
  • Yang Guo
    Innovation Research Institute of Combined Acupuncture and Medicine, Shaanxi University of CM, Xianyang 712046, China.
  • Kang Gao
    National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China.
  • Kecheng Yang
    National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China.
  • Xu Gao
    National Research Base of Intelligent Manufacturing Service, School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China; Chongqing Water Group Co. Ltd., Chongqing, 400042, China. Electronic address: hughgao@outlook.com.
  • DeZhong Yao
    The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
  • Wanting Chen
    School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
  • Mengfan Yan
    School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
  • Gang Wu
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, P. R. China.

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