Protein-ligand binding affinity prediction using multi-instance learning with docking structures.

Journal: Frontiers in pharmacology
Published Date: Jan 3, 2025

Abstract

INTRODUCTION: Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods.

Authors

  • Hyojin Kim
    Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, United States.
  • Heesung Shim
    Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA, United States.
  • Aditya Ranganath
    Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, United States.
  • Stewart He
    Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United States.
  • Garrett Stevenson
    Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, United States.
  • Jonathan E Allen
    Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United States.

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