DeepRank

Deep learning framework for data mining protein-protein interactions using CNN

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1052 commits | Last update: April 12, 2021

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What DeepRank can do for you

  • Predefined atom-level and residue-level PPI feature types, e.g. atomic density, vdw energy, residue contacts, PSSM, etc.
  • Predefined target types, e.g. binary class, CAPRI categories, DockQ, RMSD, FNAT, etc.
  • Flexible definition of both new features and targets
  • 3D grid feature mapping
  • Efficient data storage in HDF5 format
  • Support both classification and regression (based on PyTorch)

DeepRank is a general, configurable deep learning framework for data mining protein-protein interactions (PPIs) using 3D convolutional neural networks (CNNs).

DeepRank contains useful APIs for pre-processing PPIs data, computing features and targets, as well as training and testing CNN models.

Features:

  • Predefined atom-level and residue-level PPI feature types, e.g. atomic density, vdw energy, residue contacts, PSSM, etc.
  • Predefined target types, e.g. binary class, CAPRI categories, DockQ, RMSD, FNAT, etc.
  • Flexible definition of both new features and targets
  • 3D grid feature mapping
  • Efficient data storage in HDF5 format
  • Support both classification and regression (based on PyTorch)
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Tags
  • Big data
  • Machine learning
  • Optimized data handling
Programming Language
  • Python
License
  • Apache-2.0
Source code

Participating organizations

Contributors

  • Nicolas Renaud
    Netherlands eScience Center
  • Cunliang Geng
    Netherlands eScience Center
  • Li Xue
    Radboud University Medical Center
  • Sonja Georgievska
    Netherlands eScience Center
  • Dario Marzella
    Radboud University Medical Center
  • Lars Ridder
    Netherlands eScience Center
  • Francesco Ambrosetti
    Utrecht University
  • Alexandre M.J.J. Bonvin
    Utrecht University
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Contact person
Nicolas Renaud
Netherlands eScience Center

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