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Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention.

Zachary SmithMichael StrobelBodhi P VaniPratyush Tiwary
Published in: Journal of chemical information and modeling (2024)
Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery, but it remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here, we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from data set preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.
Keyphrases
  • neural network
  • working memory
  • drug discovery
  • healthcare
  • convolutional neural network
  • electronic health record
  • high resolution
  • small molecule
  • big data
  • simultaneous determination
  • tandem mass spectrometry