OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

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

Abstract

MOTIVATION: Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation.

Authors

  • Yueming Yin
    School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
  • Haifeng Hu
    School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China.
  • Jitao Yang
    School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
  • Chun Ye
    School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
  • Wilson Wen Bin Goh
    School of Biological Sciences, Nanyang Technological University, Singapore 637551, Republic of Singapore. Electronic address: wilsongoh@ntu.edu.sg.
  • Adams Wai-Kin Kong
    Rolls-Royce Corporate Lab, Nanyang Technological University, Singapore 637551, Singapore.
  • Jiansheng Wu
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) 30 South Puzhu Road Nanjing 211816 P. R. China.

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