Update paper/paper.md

Co-authored-by: Dani Bodor <[email protected]>

paper/paper.md

@@ -53,7 +53,7 @@ bibliography: paper.bib
 # Summary
 [comment]: <> (CHECK FOR AUTHORS: Do the summary describe the high-level functionality and purpose of the software for a diverse, non-specialist audience?)
 
-We present DeepRank2, a deep learning (DL) framework geared towards making predictions on 3D protein structures for variety of biologically relevant applications. Our software can be used for predicting structural properties in drug design, immunotherapy, or designing novel proteins, among other fields. DeepRank2 allows for transformation and storage of 3D representations of both protein-protein interfaces (PPIs) and protein single residue variants (SRVs) into either graphs or volumetric grids containing structural and physico-chemical information. These can be used for training neural networks for a variety of patterns of interest, using either our pre-implemented training pipeline for graph neural networks (GNNs) and convolutional neural networks (CNNs) or external pipelines. The entire framework flowchart is visualized in \autoref{fig:flowchart}. The package is fully open-source, follows the community-endorsed FAIR principles for research software, provides user-friendly APIs, publicily available [documentation](https://deeprank2.readthedocs.io/en/latest/), and in-depth [tutorials](https://github.com/DeepRank/deeprank2/blob/main/tutorials/TUTORIAL.md).
+We present DeepRank2, a deep learning (DL) framework geared towards making predictions on 3D protein structures for variety of biologically relevant applications. Our software can be used for predicting structural properties in drug design, immunotherapy, or designing novel proteins, among other fields. DeepRank2 allows for transformation and storage of 3D representations of both protein-protein interfaces (PPIs) and protein single residue variants (SRVs) into either graphs or volumetric grids containing structural and physico-chemical information. These can be used for training neural networks for a variety of patterns of interest, using either our pre-implemented training pipeline for graph neural networks (GNNs) or convolutional neural networks (CNNs) or external pipelines. The entire framework flowchart is visualized in \autoref{fig:flowchart}. The package is fully open-source, follows the community-endorsed FAIR principles for research software, provides user-friendly APIs, publicily available [documentation](https://deeprank2.readthedocs.io/en/latest/), and in-depth [tutorials](https://github.com/DeepRank/deeprank2/blob/main/tutorials/TUTORIAL.md).
 
 [comment]: <> (CHECK FOR AUTHORS: Do the authors clearly state what problems the software is designed to solve and who the target audience is?)
 [comment]: <> (CHECK FOR AUTHORS: Do the authors describe how this software compares to other commonly-used packages?)