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DeepRank2

DeepRank2 is an open-source deep learning framework for data mining of protein-protein interfaces or single-residue missense variants. This package is an improved and unified version of three previously developed packages: DeepRank, DeepRank-GNN and DeepRank-Mut.

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What DeepRank2 can do for you

DeepRank2 is an open-source deep learning framework for data mining of protein-protein interfaces or single-residue missense variants. This package is an improved and unified version of three previously developed packages: DeepRank, DeepRank-GNN and DeepRank-Mut.

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DeepRank2 allows for transformation of (.PDB formatted) molecular data into 3D representations (either grids or graphs) containing structural and physico-chemical information, which can be used for training neural networks. DeepRank2 also offers a pre-implemented training pipeline, using either convolutional neural networks (for grids) or graph neural networks (for graphs), as well as output exporters for evaluating performances.

Main features:

  • Predefined atom-level and residue-level feature types
    • e.g. atom/residue type, charge, size, potential energy
    • All features' documentation is available here
  • Predefined target types
    • binary class, CAPRI categories, DockQ, RMSD, and FNAT
  • Flexible definition of both new features and targets
  • Features generation for both graphs and grids
  • Efficient data storage in HDF5 format
  • Support for both classification and regression (based on PyTorch and PyTorch Geometric)

DeepRank2 extensive documentation can be found here.

Participating organisations

Life Sciences
Life Sciences
Radboud University Medical Center
Netherlands eScience Center

Contributors

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