Robustly interrogating machine learning-based scoring functions: what are they learning?

Journal: Bioinformatics (Oxford, England)
Published Date: Feb 4, 2025

Abstract

MOTIVATION: Machine learning-based scoring functions (MLBSFs) have been found to exhibit inconsistent performance on different benchmarks and be prone to learning dataset bias. For the field to develop MLBSFs that learn a generalizable understanding of physics, a more rigorous understanding of how they perform is required.

Authors

  • Guy Durant
    Department of Statistics, University of Oxford, St Giles', Oxford OX1 3LB, United Kingdom.
  • Fergus Boyles
    Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
  • Kristian Birchall
    LifeArc, Stevenage SG1 2FX, United Kingdom.
  • Brian Marsden
    Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, United Kingdom.
  • Charlotte M Deane
    Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, United Kingdom.

Keywords

External Resources

Popular Topics
Recent Journals