DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 3, 2024

Abstract

MOTIVATION: Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and better highlighting active sites are also significant challenges.

Authors

  • Xiying Chen
    Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Jinsha Huang
    Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Tianqiao Shen
    Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Houjin Zhang
    Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Li Xu
    College of Acupuncture and Massage, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Min Yang
    College of Food Science and Engineering, Ocean University of China, Qingdao, 266003, Shandong, China.
  • Xiaoman Xie
    Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.
  • Yunjun Yan
    Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Jinyong Yan
    Key Lab of Molecular Biophysics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Keywords

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