CAPLA: improved prediction of protein-ligand binding affinity by a deep learning approach based on a cross-attention mechanism.

Journal: Bioinformatics (Oxford, England)
Published Date: Feb 3, 2023

Abstract

MOTIVATION: Accurate and rapid prediction of protein-ligand binding affinity is a great challenge currently encountered in drug discovery. Recent advances have manifested a promising alternative in applying deep learning-based computational approaches for accurately quantifying binding affinity. The structure complementarity between protein-binding pocket and ligand has a great effect on the binding strength between a protein and a ligand, but most of existing deep learning approaches usually extracted the features of pocket and ligand by these two detached modules.

Authors

  • Zhi Jin
  • Tingfang Wu
    1 Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China.
  • Taoning Chen
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Deng Pan
    Hefei National Laboratory for Physical Sciences at the Microscale, Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China.
  • Xuejiao Wang
    School of Literature and Journalism, Sanjiang University, Nanjing, Jiangsu 210012, China.
  • Jingxin Xie
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Lijun Quan
    School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • Qiang Lyu
    Department of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, 215006, China.

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