diff --git a/docs/index.rst b/docs/index.rst
index fe3d4084..29a7cb0c 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -4,14 +4,19 @@ DeepRank2 |version| 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.
+As input, DeepRank2 takes `PDB-formatted`_ atomic structures, and map them to graphs, where nodes can represent either residues or atoms, as chosen by the user, and edges represent the interactions between them. The user can configure two types of 3D structures as input for the featurization phase:
+
+- PPIs, for mining interaction patterns within protein-protein complexes, implemented by the `ProteinProteinInterfaceQuery` class;
+- SRVs, for mining mutation phenotypes within protein structures, implemented by the `SingleResidueVariantQuery` class.
+
+The physico-chemical and geometrical features are then computed and assigned to each node and edge. The user can choose which features to generate from several pre-existing options defined in the package, or define custom features modules, as explained in the documentation. The graphs can then be mapped to 3D-grids as well. The generated data can be used for training neural networks. DeepRank2 also offers a pre-implemented training pipeline, using either `convolutional neural networks`_ (for 3D-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
+* Features generation for both graphs and 3D-grids
 * Efficient data storage in HDF5 format
 * Support both classification and regression (based on `PyTorch`_ and `PyTorch Geometric`_)
 
@@ -24,6 +29,7 @@ Main features:
 .. _Docking scores: https://deeprank2.readthedocs.io/en/latest/docking.html
 .. _PyTorch: https://pytorch.org/docs/stable/index.html
 .. _PyTorch Geometric: https://pytorch-geometric.readthedocs.io/en/latest/
+.. _PDB-formatted: https://www.cgl.ucsf.edu/chimera/docs/UsersGuide/tutorials/pdbintro.html
 
 Getting started
 ===========