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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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.