A small step toward generalizability: training a machine learning scoring function for structure-based virtual screening

Geahča maid

• Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding
  Dahkki: Hadfield, TE, et al.
  Almmustuhtton: (2023)
• Data set augmentation allows deep learning-based virtual screening to better generalize to unseen target classes and highlight important binding interactions
  Dahkki: Scantlebury, J, et al.
  Almmustuhtton: (2020)
• Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding
  Dahkki: Thomas E. Hadfield, et al.
  Almmustuhtton: (2023-09-01)
• Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures
  Dahkki: Carbery, A, et al.
  Almmustuhtton: (2024)
• Fragment libraries designed to be functionally diverse recover protein binding information more efficiently than standard structurally diverse libraries
  Dahkki: Carbery, A, et al.
  Almmustuhtton: (2022)