DeepRank2 3.0.1 documentation

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

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 under Features notes)

  • Predefined target types (binary class, CAPRI categories, DockQ, RMSD, and FNAT, detailed docking scores documentation is available under Docking scores notes)

  • Flexible definition of both new features and targets

  • Features generation for both graphs and grids

  • Efficient data storage in HDF5 format

  • Support both classification and regression (based on PyTorch and PyTorch Geometric)

Getting started

Table of contents

Get DeepRank2 installed on your computer.

Get started

Understand how to use DeepRank2 and how it can help you.



Get a detailed overview about nodes’ and edges’ features implemented in the package.

Docking scores

Get a detailed overview about PPIs’ docking metrics implemented in the package.

Package reference

deeprank2 package

This section documents the DeepRank2 API.

Indices and tables