SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.

Journal: Journal of advanced research
Published Date:

Abstract

INTRODUCTION: The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machine-learning scoring functions re-evaluate the output of molecular docking to achieve more accurate predictions. However, previous scoring functions were trained on crystalised protein-ligand complexes and datasets of decoys. The limited availability of crystal structures and biases in the decoy datasets can lower the performance of scoring functions.

Authors

  • Miles McGibbon
    Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK.
  • Sam Money-Kyrle
    Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK.
  • Vincent Blay
    Fisher College of Business , The Ohio State University , Gerlach Hall, 2108 Neil Avenue, Columbus , Ohio 43210 , United States.
  • Douglas R Houston
    Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, UK. Electronic address: DouglasR.Houston@ed.ac.uk.