Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
Authors: Arian Rokkum Jamasb
Ramon Viñas Torné
Eric J Ma
Yuanqi Du
Charles Harris
Kexin Huang
Dominic Hall
Pietro Lio
Tom Leon Blundell
Venue: NeurIPS 2022 Accept (2022)
Keywords: Protein
Drug Discovery
Geometric Deep Learning
Biomolecules

Abstract: Geometric deep learning has broad applications in biology, a domain where relational structure in data is often intrinsic to modelling  the underlying phenomena. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of biomolecular structures, such as proteins, nucleic acids and small molecules, and biological interaction networks for computational analysis and machine learning. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the AlphaFold Structure Database, chemical data from ZINC and ChEMBL, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, Jraph, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientific computing tools and libraries.  Graphein is designed to be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughput manner. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery. 

Availability and implementation: Graphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License at the following URL: https://anonymous.4open.science/r/graphein-3472/README.md

Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks
Arian R. Jamasb 1.2% Ramon Viñas Eric J. Ma", Charlie Harris Kexin Huang',
Dominic Hall", Pietro Li6"*, Tom Blundell- Department of Biochemistry, University of Cambridge
2 Department of Computer Science & Technology, University of Cambridge PyMC Labs
4 Department of Computer Science, Stanford University
Abstract
Geometric deep learning has broad applications in biology, domain where relational structure data often intrinsic to modelling the underlying phenomena. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scareity of appropriate datasets accessible domain specialists and machine learning researchers alike. To address this, we introduce Graphein as turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and flexible manner. Graphein is Python library for constructing graph and surface-mesh representations of biomolecular structures, such as proteins, nucleic acids and small molecules, and biological interaction networks for computational analysis and machine learning. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the AlphaFold Structure Database, chemical data from ZINC and ChEMBL, and for biomolecular interaction networks from STRINGdb. BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, Jraph, PyTorch Geometric and PyTorch3D though remains framework agnostic as built on top the PyData ecosystem to enable inter-operability with scientific computing tools and libraries. Graphein designed be highly flexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools for preparing experimental files. Graphein facilitates network-based, graph-theoretic and topological analyses of structural and interaction datasets in a high-throughh manner. We envision that Graphein will facilitate developments in computational biology, graph representation learning and drug discovery.
Availability and implementation: Graphein is written in Python. Source code, example usage and tutorials, datasets, and documentation are made freely available under the MIT License the following URL: https: pattps://graphese h/tp///ggaap.nn..eee ai
"To whom correspondence should be addressed: arj396cam.c uk, p1219@c1. cam. ac.uk
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

---

AlphaFold Protein Structure Dstabase
Interactome
PDB STRING ZINC
BloGRID
TRRUST
RegNetwork
ChEMBL
Graphein
Raw Biological Forms
Machine Learning-Reen Ready Datasets
Figure 1: Graphein rapidly transforms and integrates raw biological and chemical data into actionable
machine learning-ready datasets.
1
Introduction
The functional roles of macromolecules are intricately tied to the complex three dimensional struc- tures they adopt. Many biological functions mediated by interacting biomolecular entities, often through direct physical contacts governed by their 3D structures. Recent developments in protein fold- ing have given rise to explosion of structural data available to machine learning practitioners [1, 2]. Highly-accurate protein structure prediction using AlphaFold2 has been applied at the proteome scale humans and 20 key model organisms [1, 3]. Recent work making of these data have demonstrated the power of GNNs applied to protein structures methods over models trained on large corpora of sequences alone [4] for protein representation learning and highlighted the importance of carefully considering the graph representation. We anticipate significant growth in the availability of biomolecular structural and interaction data in the coming years as both computational and experimen- tal techniques mature. In particular, we identify structural interactomics as an emerging application area for geometric deep learning as sparse experimental structural coverage of interactomes can be annotated with modelled macromolecular structures. However, the question of how to best leverage and integrate these data with other modalities remains. recent review biomedical knowledge graph datasets identifies graph composition, feature and metadata incorporation and reproducibility as key challenges [5]; we believe these are vital considerations for structural and interactomic data. To address these issues, we present Graphein, a flexible tool that provides greater control over the data engineering and featurisation process of structural data, abstracting away cumbersome data preparation tasks and facilitating reproducible research.
Graphein provides bridge for geometric deep learning into structural interactomics through conve- nient structure retrieval and graph creation tools. Our library interfaces with several protein structure databases leverage decades of structural biology research as well as recent developments protein folding. These advances have resulted in large pool of experimentally-ddtennee -eetemmned and modelled protein structures, with massive potential inform future research [6. 1]. To realise this potential, Graphein additionally represents these graphs at different levels of granularity, including atom, residue, secondary structure and chain-level graphs, and populates the corresponding node and edge attributes with informative features, enabling wide range of downstream applications of geometric deep learning.
Representing Structural Biomolecular Data It is not yet clear how best represent these data in machine learning experiments. It has been shown that structure- methods frequently outperform eqquence-based methods and that the choice of architecture shows strong task dependency [7]. 3D Convolutional Neural Networks (3DCNNs) have been routinely applied to grid-structured repre- sentations of protein structures and sequence-based methods have proved commonplace [8, 10]. sarface-based methods have proved effective for predicting protein-proted and protein-ligand interac- tions [11, 12]. Nonetheless, these representations fail to capture relational information in the context of intramolecular contacts and the internal chemistry of the biomolecular structures. Furthermore, these methods are often computationally inefficient due to convolving over large regions of empty space, and computational constraints often require the volume of the protein considered to regions of interest, thereby losing global structural information. For instance, in the case of protein-ligand interaction and binding affinity prediction, central tasks in data-driven drug discovery, this often takes

---

the form of restricting the volume to be centred on a binding pocket, thereby losing information about allosteric sites on the protein and possible conformational rearrangements that contribute to molecular recognition. Furthermore, 3D volumetric representations are not rotationally invariant, deficiency that is often mitigated using costly data augmentation techniques. Graphs suffer relatively less from these problems as they are translationally and rotationally invariant. Structural descriptors of position can be leveraged and meaningfully exploited by architectures such as Equivariant Neural Networks (ENNs), which ensure geometric transformations applied to their inputs correspond to well-defined transformations of the outputs.
Macromolecular structures and biological interaction networks can naturally be represented as graphs different levels of gramularity and abstraction with edges denoting intramolecular, regulatory and functional interactions or spatial relationships. The graph structure can further be elaborated by assigning metadata and numerical features to nodes and edges as well as the whole graph. These features can represent, for instance, chemical properties of residues or atoms, secondary structure assignments or solvent accessibility metrics of the residue or descriptions of geometry. Edge features can include bond interaction types as well as distances. Graph features can include functional annotations or sequence-base descriptors. In the context of interaction networks, structural data can be overlaid on protein nodes providing a multi-scale view of biological systems and function. By providing a toolkit for integrating data across these domains Graphein provides a bridge for geometric deep learning into structural interactomics.
2
Related Work
Geometric Deep Learning Tools Geometric deep learning methods have demonstrated their suit- ability for tasks across domains. In part, this has been fuelled by the development of libraries that provide easy access to non-Euclidean data objects and models from the literature. Deep Graph Library (DGL) [13] and PyTorch Geometric [14] are the main open-source frameworks built for Py Torch
[15]. Other tools include: Graph Nets [16] for Sonnet [17]/Tensorflow [18] and Jraph [19] for JAX [20]. Whilst several of these provide datasets and simple featurisation schemes relevant to the life sciences, these are typically focussed on small molecules [13, 14]. However, data preparation for geometric deep learning in structural biology and interactomics is yet to receive the same attention and tools addressing this bottleneck can greatly accelerate research in these domains [21].
Datasets and Benchmarks In-built dataset support is a common feature of geometric deep learning frameworks. More specialised libraries, such as DGL-LLfeSci, DeepChem and TorchDrug, provide datasets, featurisation, neural network layers and pre-trained models for tasks involving small molecules in the life sciences, computational chemistry and drug development [22, 23, 24]. TorchDrug and DeepChem provide reinforcement learning environments to fine tune generative models for physicochemical properties such as drug-likeness (QED) and lipophilicity (LogP). Therapeutics Data Commons provides raw datasets for small molecule and biologics tasks [25]. However, none of these tools address the transformation of macromolecular structures into machine learning-ready formats or provide comprehensive utilities for the generation of new datasets.
Biomolecular tasks are included in many graph representation learning benchmarks. The Open Graph Benchmark (OGB) includes graph property prediction tasks on small molecules, link pre- diction tasks (ogbl-ppa) based on protein-protein interaction prediction and biomedical knowl- edge graph (ogbl-biokg), and a node classification task based on prediction of protein function (ogon-pun -rooteino [26]. The TUDataset contains three biologically- mottvvated benchmark datasets for graph classification, (PROTEINS, ENZYMES and DD) relevant to applications in structural biology [27]. For PROTEINS and DD the goal predict whether or not protein an enzyme and these are derived from the same data under differing graph construction schemes [28, 29]. ENZYMES provides a task based on assigning Enzyme Commission (EC) numbers to graph representations of enzyme struc- tures derived from the BRENDA database [30]. However, these collections have been abstracted away from the underlying biological data and computing additional features and metadata is cumbersome. More recently, ATOM3D provides collection of benchmark datasets for
tasks on biomolecules and show leveraging structural information consistently improves performance, and that the choice of architecture significantly impacts performance depending on the task context [7].
3

---

Tools for Working with Biomolecular Graphs Whilst tools exist for transforming protein struc- tures into graphs, they typically focus on visualisation and traditional bioinformatics analyses for spe- cific use-cases and cannot be conveniently used for developing deep learning models [31]. GraProStr web-server that enables users submit structures for conversion into graph which can be downloaded as textfiles [32]. This provides users with limited control over the construction process, low-throughput and limited featurisation support. Furthermore GraProStr provides no utilities for machine learning unifying structural and interactomic data. Mayavi, and GSP4PDB & LIGPLOT provide utilities for visualising protein structures and protein-ligand interaction as graphs, respec- tively [33, 34, 35]. Bionoi library for representing pootein-ligante interactions voronoi diagram images specifically for upplications in machine learning [36]. PyUUL is a recent tool designed for transforming biological structures into formats suitable for computer vision tasks, such voxels and point clouds. However, graph-based representations and interaction data are not addressed [21]. J. The lack of fine-gr fine-grained control over the construction and featurisation, public APIs for high-throughput programmatic access, ease of integrating data modalities, and incompatibility with deep learning libraries motivated the development of Graphein as machine learning-firs library.
3 Graphein
Graphein provides utilitics for constructing geometric representations of protein and RNA structures, small moleculure, protein-protein interaction networks, and gene regulatory networks. The library provides high- and low-level APIs, appropriate for both novice and experienced users. The high- level API constructs geometric representations of structural and interaction data from standard biological identifiers. The low-level API offers a fine-grained customisation of the graph selection from the input data, allowing users define their own data preparation, graph construction and featurisation functions in consistent manner. Graphein is built on the PyData Stack to allow for easy inter-operability with standard scientific computing tools and deep learning framework agnosticism. Graphein is organised into submodules for each of the supported modalities (Figure 2).
Structural Data
Interaction Data
Protein-Protein Interactions
Gene Regulatory Networks
Protein
RNA
Molecules
Sequences Dotbrackets
CHEMBL
STRING
RegNetwork
AuaaFaeTozee Soscher Decabon
Data
PDB PDB
ZINC
BioGRID
TRRUST
Node Representation
Residue
Mucledtide
Node Features
Physicochemical and structural properties, Embeddlings
Base Type Postion
Atomic Properties Position
Protein graphs Sequence features
Protein graphs RNA graphs Sequence features
Edge Types
Intramolecufar
Phosphodiester Pseudoknot
Evidence Type, Functional, Physical
Regulatory Evidence type
Spatial
Graph-Level Features
Functional annotations, Sequence-baase based features, Embeddiings
Functional annotations, Sequence- based features
Chemical properties Bioactivity metadata
Functional annotations, Metacitta
Functional annotations, Metadata
Figure 2: Overview of graph and mesh construction and featurisation schemes for data modalities sup- ported by Graphein. Modules are inter-operable allowing protein or RNA structure graph construction to be applied to nodes in regulatory networks.
3.1 Protein Structure Graphs
Graphein interfaces with the PDB the AlphaFold Structure Database to create geometric rep- resentations of protein structures. Furthermore, users can supply their own .dbb files, enabling pre-processing with standard bioinformatics tools and pipelines. overview of featurisation schemes is provided in Supplementary Information A.

---

Residue Graphs
Secondary Structure Graphs
Subgraphs
Surfaces
Insondleca Cortact
De suray
Min, R-5
10th Kid, UMa12
Chain Graphs
Atomic Graphs
Point Clouds
Ceronce
Figure 3: Representation types for structural data processed with Graphein. Plots generated with
Graphein's 's visualisation tools.
Node Representations Graphs can be constructed for all chains contained within polypeptide structure, or for user-defined selection of chains. This is useful in contexts where regions of interest on a protein may localised single chain. For residue-level graphs, users can choose between atom-based positional information, or sidechain centroids. Sidechain centroids are calculated as the centre of gravity of the deprotonated residue. Residue-level graph nodes can be featurised with a one-hot encoding of amino acid type, physicochemical and biochemical properties retrieved from the ExPaSY ProtScale [37] which includes 61 descriptors such as iso-electric points, mutability and transmembrane tendencies. Additional numerical features can be retrieved from AAIndex [38]. Low-dimeesionall embeddings of amino acid physicochemical properties are provided from Kidera et al. [39] and Meiler et al [40]. In addition to fixed embeddings, sequence embeddings from large pre-trained language models, such as the ESM-Ib Transformer model [41] and Vec [42]. Secondary structural information can be included via one-hot encoded representation of eight state secondary structure and solvent accessibility metrics (ASA, RSA, SSA) computed by DSSP [43]. 2; y, = positions added node features. Vector-based features capturing important aspects of protein structure, such as sidechain position and dynamics derived from Normal Mode Analysis, provided. These have been shown to be highly informative features [44] and have the potential to be meaningfully incorporated in equivariant neural network models. Functionality for user-defined node edge features is also provided with useful utilities allowing for computation or aggregation of features over constituent chains. Figure illustrates an overview of the mesh and graph construction methods as well as the node and edge featurisation schemes; Figure 3 shows example visualisations of graph and meshes produced by Graphein.
be retrieved
Edge Representations Graphein provides utilities in the high-level API for a number of edge- construction schemes. The low-level API provides simple and intuitive way for users to define novel edge construction schemes. Edge construction methods are organised into distance-based intramolecular interaction-basede and atomic structure-based submodules. Each of these edge construction methods are composable to produce multirclational graphs. This is particularly useful for models that operate different levels to capture varying aspects of the underlying network.
Functionality for computing intramolecular graph edges is provided through distance-based heuristics as well as through an optional dependency, GetContacts [45]. Euclidean distance- based edges can be computed with user-eefined threshold. Functionality for constructing k-nearest neighbour graphs, where two vertices are connected by an edge if they are among the k-nearest neighbours by Euclidean distance included. Graph edges can also added on the basis of the Delaunay triangulation. Delaunay triangles correspond to joining points that share face in the 3D Voronoi diagram of the protein structures. For distance- based edges, Long Interaction Network (LIN) parameter controls the minimum required separation in the amino acid sequence for edge creation. This is useful to
5

---

reduce the number of noisy edges under distance-based edge creation schemes. Edge featurisation for atom-level graphs is provided by annotations of bond type and ring status.
Protein-Ligand Graphs & Subgraphs Graphein provides powerful selection methods for extract- ing subgraphs from regions of interest within macromolecular structures. Proteins are often large and the region of the structure relevant to given task may be localised to region of the structure. For instance, we can extract binding pocket subgraphs based on known ligand pose as well as subgraphs of interfaces in the case of complexed structures, surface-subgrapee that retain only solvent-exposed residues.
3.2 Protein Structure Meshes
Geometric deep learning applied to surface representations of protein structures have demonstrated promise on a variety tasks in the context of structural biology and structural interactomics 461, The protein structure mesh module consists of wrapper for PyMOL, commonplace molecular informatics visualisation tool, and Pytorch3D [47]. PyMol used to produce . .Obj file from either a PDB accession code provided .PDB file, enabling the use of pre- processed structures. Pytorch3D is used to produce a tensor-based representation of the protein surface as vertices and faces. Users can pass any desired parameters commands controlling the surface calculation to PyMol via configuration object. These parameters include specifying solvent inclusion, solvent probe radius, surface type, and resolution. We provide sane defaults for first-time users. To our knowledge, this is the first application of Torrch3D for protein structure data.
3.3 Molecules
Featurisation schemes and computational analysis of small molecular graphs relatively mature area of research. Typically, nodes represent atoms and edges denote the chemical bonding structure between them. Junction trees compact representation of molecular graphs, coarsening the graph such that nodes represent molecular substructures [48]. Graphein provides utilities for working with both of these representations and conformer generation enable models operating molecules embedding in 3D space which has been shown to be highly effective for representation learning [49]. Graphein provides extensive featurisation options for molecules and also supports working with molecular fragments enabling workflows in fragment- based drug design. Molecular structure data can be retrieved from both ZINC [50] and ChEMBL [51]. Furthermore, the wealth of curated metadata, such as bioactivity, from ChEMBL can be queried and used in fcaturisation.
3.4 RNA Structures
Ribonueleic Acid (RNA) is nucleotide biopolymer capable of forming higher-order structural ar- rangements through self-association mediated by complementary base pairing interactions. Graphein provides utilities for constructing secondary structure graphs and 3D of RNA structures, taking as input crystallographic structure or an RNA sequence and an associated string representation of the secondary structure in dotbracket notation [52]. Graphs can be constructed using two types of bonding between nucleotides: phosphodiester bonds between adjacent bases, and base-pairing interactions between complementary bases specified by the dotbracket string (Figure 4). Graphein also supports addition of pseudoknots structural motifs composed of interactions between intercalated hairpin loops specified in the dotbracket notation.
3.5 Interaction Networks
Interactomics presents clear application of geometric deep learning as these data are fundamentally relational in structure. Biomolecular entities can be represented nodes, and their associated func- tional relationships and physical interactions can be represented as edges with associated metadata, such as the direction and nature of regulation. For a full discussion of applications, datasets and mod- elling techniques we refer readers to the reviews by [5] and [53]. Graphein implements interaction graph construction from protein-protein interaction and gene regulatory network databases. Interac- tion graphs integrate networks from several sources and can be constructed in highly customisable way (see Supplementary Information B, for a summary of user-definable parameters) with the ability to overlay structural models for the biomolecular species involved.

---

[[]
Figure 4: Example RNA Secondary Structure Graph. RNA Secondary structures can be represented as dotbracket strings and multi-relational graphs. Blue edges indicate phosphodiester backbone linkages, red edges indicate base-paie aase-pairing interactions and green edges indicate pseudoknot pairings.
3.5.1 Protein-Protein Interaction Networks
Many of the functional roles of proteins carried out by larger assemblies of protein complexes and many biological processes are regulated through interactions mediated by physical contacts. Understanding these functions is central to characterising healthy and diseased states of biological systems. Graphein interfaces with widely-used databases of biomolecular interaction data for easy retrieval and construction of graph-based representations of pootein-proteii interactions.
STRING a database of more than 20 billion known and predicted functional and direct physical protein-protedd interactions between 67.6 million proteins across 14,094 organisms [54]. Predicted interactions in STRING are derived from genomic context, high-throughput experimental procedures, conservation of co-expression, text-mining of the literature and aggregation from other databases. STRING is made freely available by the original authors under a Creative Commons BY 4.0 license.
BioGRID is database that archives protein interaction data from model organisms and humans, curated from high-throughput studies and individual studies. The database contains 2,127,726 protein and genetic interactions curated from 77,696 publications [55]. BioGRID is made available for academic and commercial use by the original authors under the MIT License.
3.5.2 Gene Regulatory Networks
Gene regulatory networks (GRNs) consist of collections of genes, transcription factors (TFs) and other regulatory elements, and their associated regulatory interactions. Reconstructing transcriptional regulatory networks is a long-standing problem computational biology its own right due to relevance to characterising healthy and diseased states of cells, and these data can provide meaningful signal in other contexts such as multi-moda, modelling of biological systems and phenomena. Graphein supports GRN graph construction from two widely- used databases, allowing construct graph representations of these networks.
Protein Structure Graph
Heteromeric Protein Complex Graph
easily unify datasets and
is database of regulatory interactions for human and mouse interactomes curated from literature via a sentence- based text-mining approach [56]. The current release contains 8,427 / 6,490 regulatory interactions with associated regulatory directions (activatioo//reressio)) factors and 2,067 1,629 conttranscription factor genes for hu- mans and mice, respectively. TRRUST is made freely available by the original authors for non-commercial research under a Creative Commons License.
TRRUST
Graph
Figure 5: Graphein can facilitate the integration of structural and biomolecular interaction data to en- able geometric deep learning re- search in structural interactomics. 3D visualisations graphs are gen- erated using Graphein.
over 795/ / 827 transcription
4.0 International
RegNetwork is database of transcription-facto and miRNA mediated regulatory interactions for humans and mice [57]. RegNetwork an aggregation of 25 source databases from which the regulatory network is populated and annotated. The latest release contains 14,981 / 94,876 TF-gene,

---

361 129 TF-TF, 21,744/ 0///44//2,,557 25,574 Ti-mNNN, 171,477 176,512 miRNA- -eene and 25,854 26,545 miRNA-TF interactions over 1,456/ 1.328 transcription factors, 1,904 1,290 miRNAs and 19,719 / 18, 18,120 genes for humans and mice, respectively. The dataset is made publicly available by the original authors.
Datasets
As example workflows, we make available two graph-based protein structure datasets focussed on tasks where relational inductive biases appear intuitively useful and demonstrate how Graphein can help formulate different tasks from the same underlying dataset.
PPISP- Protein Protein Interaction Site Prediction The first, based on the collections outlined in [58], consists of 420 protein structures, with node labels indicating whether a residue is involved in protein-proteed interaction task central to structural interactomics [59]. The data originate from co-crystallised structures of the complexes in the RCSB PDB. The authors make available set of additional node features based on Position-Speciffe Scoring Matrices (PSSMs), providing evolutionary context as to which protein-protein interaction sites are typically conserved, which can be incorporated with the structural node features calculated by Graphein. This dataset was used in [60] in conjunction with Graphein to compute the protein structure graph inputs to a Message-Passing Neural Process model which achieved state-of-the-ar performance.
PSCDB Protein Structural Change Database The second dataset, based on Protein Structural Change Database (PSCDB) [61], consists of 904 paired examples bound and unbound protein struc- tures that undergo 7 classes of conformational rearrangement motion. Prediction of conformational rearrangement upon ligand binding a longstanding problem computational structural biology and has significant implications for drug discovery and development. Two tasks can be formulated with this dataset. The first is the graph classification task of predicting the type of motion protein undergoes upon ligand binding, the second is framing prediction of the rearrangement itself as an edge prediction task between paired bound and unbound protein structure graphs. These tasks provide utility in improving understanding of protein structural dynamics in drug development, where molecules are typically docked into largely rigid structures with limited flexibility in the binding pockets in high-throughput in silico screens. PSCDB is made publicly available by the original authors and we provide a processed version our repository.
ccPDB We derive four datasets, each with a graph and node classification task from the ccPDB [62]. The ccPDB provides collections of protein structures and annotations of interactions with various molecular species. The proteins are high-quality, non-redundant sets (25% sequence identity) with maximum resolution of A, minimum sequence length of 80 residues. Node-level annotations of interaction are provided in each case with the cutoff set at Á. ccPDB is made freely available online.
PROTEINS_ _MEAAL contains protein structures that bind 7 types of metal ions (Fe, Mg, Ca, Mn, Zn, Co, Ni; 215 1,908 / 1,402 521 / 1,660 201 / 355).
PROTEINS_NUCLES NUCLEOTIDES contains protein structures that bind 8 species of nucleotides (ATP, ADP, GTP, GDP, NAD, FAD, FMN, UDP; n = 313 353 / 83 120 140 / 172 117 68)
PROTEINS_NUCLELS contains protein structures that bind DNA RNA polymers (n 560 /415).
PROTEINS LIGGADD
LIGAND contains protein structures that bind species of ligands (SO,, PO NAG, HEM, BME, EDO, PLP; n 3 1299 / 727 176 / 191 1507 / 65).
5 Machine Learning Utilities
Here we introduce the different machine learning utilities that Graphein provides. They include modules to convert graph object into machine learning-ready formats, manipulate and visualise graphs, appropriately split datasets to avoid data-lcaking, several usage tutorials and template notebooks.

---

Conversion & Dataset Classes Convenience utilities for converting between NetworkX [63] graph objects and commonly-usee geometric deep learning library data objects are provided for DGL, PyTorch Geometric and Jraph. Underlying graph objects are based on NetworkX, enabling conversion to other formats. We provide wrapped Py PyTorch Geometric dataset classes which handle downloading of structures and preparation of graphs. As the required user inputs are list of identifiers, labels (optionally) and a Graphein config, these are very lightweight formats for sharing entire pre-processing pipelines that can be readily reused and adapted by others.
Adjacency Tensors, Diffusion Matrices & Line Graphs Graphein provides utilities for compu- tation of diffusion matrices (and related adjacency matrices) to (1) facilitate exploration of biological data with models that leverage these representations, and (2) aid in the construction of diffusion matrices for graph neural networks. We also provide utilities for computing line graphs. Performing edge message passing on line graphs [64] has been shown to be highly effective in representation learning 3D molecules and protein structures [4].
Built-in interactive tools are provided for each of the modalities supported to allow inspection of data in pre and post-processing. Further utilities for analysing and plotting graph properties are provided.
Visualisation
Data Splitting Splitting datasets of protein structures requires some consideration to avoid leaking data through homologous proteins as proteins with low-levels of sequence identity can adopt very similar folds. In most cases (i.e. where the data have not already been filtered for redundancy), random sampling is unsuitable as the data are unlikely to be independent due to evolutionary relationships. We provide utilities for splitting and clustering datasets based sequence homology using BLAST.
6 Usage
Example usage and workflows are provided in the documentation. Examples and tutorials are provided as runnable notebooks detailing use of the high and low-level APIs for the data modalities currently supported by Graphein, and the ease of ingesting novel structural datasets into a suite of geometric deep learning models. Source code is made at: https: http///ggaa...iee/eeee ai.
Conclusion
Geometric deep learning has shown promise in computational biology and structural biology. How- ever, the availability of processed datasets is a research bottleneck. Graphein is a Python library designed to facilitate construction of datasets for geometric deep learning applied to biomolecular structures and interactions. By providing tools for these modalities, we hope to facilitate research in data-driven structural interactomics. We make available two example datasets for protein-proteis interaction site prediction (node classification) and protein conformational rearrangement prediction (graph classification and edge prediction) well as extensive documentation and tutorial notebooks.
Whilst graphs are a natural representation of biological interaction data, hypergraphs may provide fideeitfieelty representation of the underlying biological relationships. Many interactions are contextual, or require multiple obligate interactors, which can be represented by hyperedges between several entities required for functional or structural relationship. We are also pursuing richer representations of dynamics, both in structural data and in interactions as these are central biological components that are beyond the scope of the present release. These features will be included in subsequent releases and the API design of Graphein makes it simple for users to write and contribute their own workflows. Graphein implements a high-level and low-level API to enable rapid and fine-grained control of data preparation. Graphein is provided as Free Open Source Software under permissive MIT License which we hope will encourage the community contribute customised workflows to the library. We hope that Graphein serves to further progress in the field and reduce friction in processing structural and interaction data for geometric deep learning. The library also provides utility in preparing protein structure and interactomics graphs for graph-theorets and topological data analyses which we hope will draw further insight from the computational biology community.

---

References
[1] John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ron- neberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zidek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Ko- ray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis. Highly accurate protein structure prediction with AlphaFold. Nature, July 2021. doi: 10.1038/s4188--P5 021-0081-9- URL. https://doi. prg/10.10 01/0///4118601110100000
[2] Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang. Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park. Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister. Christoph Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin, Paul D. Adams, Randy J. Read, and David Baker. Accurate prediction of protein structures and interactions using a three- track neural network. Science, (73(6557):871-8777 July 2021. doi: 10.1 10.1126/cienceeaabj...... URL https: //dp:::://ii... org/ 10. 1126/ science. abj8754.
[3] Kathryn Tunyasuvunakool. Jonas Adler, 7achary Wu, Tim Green, Michal Zielinski, Au- gustin Židek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J. Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A. A. Kohl, Anna Potapenko, Andrew J. Ballard, Bernardino Romera-Paredess Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W. Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, and Demis Hassabis. Highly accurate protein structure prediction for the human proteome. Nature, July 2021. doi: 10. 10:1038.4413866-2--38332,, URL
https: :/tps:://oo... http///iiio///////////o 1038/ 1038/ s41586- 021 03828-1
[4] Zuobai Zhang, Minghao Xu, Arian Jamasb, Vijil Chenthamarakshan, Aurelie Lozano. Payel Das, and Jian Tang. Protein representation learning by geometric structure pretraining, 2022.
[5] Stephen Bonner, Ian P Barrett, Cheng Ye. Rowan Swiers, Ola Engkvist, Andreas Bender, Charles Tapley Hoyt, and William Hamilton. A review of biomedical datasets relating to drug discovery: A knowledge graph perspective, 2021.
[6] Helen M. Berman, John Westbrook, Zukang Feng, Gary Gilliland, T. N. Bhat, Helge Weissig, Ilya N. Shindyalov, and Philip E. Bourne. The Protein Data Bank. Nucleic Acids Research, 28 (1):235-242, 01 2000. ISSN 0305-1048, doi: 10.1093/nar/8..122 1.235. URL https: org/ 100.199/mar// nar 235..
[7] Raphael L. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, Risi Kondor, Russ B. Altman, and Ron O. Dror. Atom3d: Tasks on molecules in three dimensions, 2020.
[8] Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes. Protein-ligand scoring with convolutional neural networks. Journal of Chemical Informa- tion and Modeling, 57(4):942-957, April 2017. doi: 10.1021/acs.jcim.66600 6b000000740. URL
https: //doi. org/ 10. 10000/aacs... jacm/..ss............mm......... 6b00740.
[9] Rin Sato and Takashi Ishida. Protein model accuracy estimation based on local structure quality assessment using 3d convolutional neural network. PLOS ONE, 14(9):e0211347, September 2019. doi: 10.137 woo.urall.poo..022..... 02213377 URL https:///oi. org/ooog//// wttrg//....//////... journal. pone. 0221347.
[10] Limeng Pu, Rajiv Gandhi Govindaraj, Jeffrey Mitchell Lemoine, Hsiao-Chun Wu, and Michal Brylinski. DeepDrug3d: Classification of ligand-binding pockets in proteins with a convolu- tional neural network. PLOS Computational Biology, 15(2)ee100611 February 2019. doi: 10. pcb. 137 1/joumall.pcbi 1006700.11.1 18. URL pastps://doos /tt.ii..g///i.e/ooooooooooo orw.oogg//ooo 1ttg//.1.11/////// 1371/journalll journalllllaao pcbr 1006718.
10

---

[11] P. Gainza, F. Sverrisson, F. Monti, E. Rodolå, D. Boscaini, M. M. Bronstein, and B. E. Correia. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods, December 2019, doi: 1..1028/44192--099006666 URL https: //doi.org/11 1038/ 000.00000000111--------066-----1
[12] Ilia Igashov, Arian R. Jamasb, Ahmed Sadek, Freyr Sverrisson, Arn Schneuing, Pictro Liö, Tom L. Blundell, Michael Bronstein, and Bruno Correia. Decoding surface fingerprints for protein-ligand interactions. April 2022. doi: 2..10112022.04.26.88899
URL https:
/ allii.org/10 1tt//10.1101111 /101/2022.... 04 26. 489341.
[13] Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J Smola, and Zheng Zhang. Deep graph library: Towards efficient and scalable deep learning on graphs. ICIR Workshop on Representation Learning on Graphs and Manifolds, 2019. URL https: ///rx//avv/ org/ abs/ 1909. 01315.
[14] Matthias Fey and Jan E. Lenssen. Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.
[15] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32:8026- 8037, 2019.
[16] Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zam- baldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo. Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu. Relational inductive biases, deep learning, and graph networks, 2018.
[17] Deepmind. Sonnet. httpp://giiee http:///thbb..coc//eeaaa////////
2017.
[18] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. Tensorflow: A system for large-scale machine learning. In /2th (USENIX) symposium on operafing systems design and implementation ({OSDI) 16), pages 265-283, 2016.
[19] Jonathan Godwin*, Thomas Keck*, Peter Battaglia, Victor Bapst, Thomas Kipf, Yujia Li, Kimberly Stachenfeld, Petar Veličković, and Alvaro Sanchez-Conzaleez. Jraph: A library for graph neural networks in jax., 2020. URL htttpp//ggiittub..commeemmmmadda jraph.
com
[20] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Sanderman-Milmn and Qiao Zhang. JAX: composable transformations of Python+NumPy programs, 2018. URL
http://github.. cttp cttp:/ooo///ooo//////o/ooo/
[21] Gabriele Orlando, Danicle Raimondi. Ramon Muran-Romaña. Yves Moreau, Joost Schymkowitz, and Frederic Rousseau. PyUUL provides an interface between biological structures and deep learning algorithms. Nature Communications, 13(1), February 2022. doi: 10.1038/<41467-0222 28327-3. URL https: htt:://i..orggg...// 10t5///0.1000///// -41467 -22-477772222222---33
[22] Mufei Li, Jinjing Zhou, Jiajing Hu, Wenxuan Fan, Yangkang Zhang, Yaxin Gu, and George Karypis. Dil-lifesci: An open-source toolkit for deep learning graphs in life science, 2021.
[23] Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu. Deep Learning for the Life Sciences. O' O'Reilly Media, 2019. https / //www/ amazon. com/ Deep-Learning- Suffe-Sciences -eciienes--Miii httpc//cooo//pp///00///////
[24] URL https://torchdrug. ai/.

---

[25] Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development. NeurIPS Track on Datasets and Benchmarks, 2021.
[26] Weihua Hu, Matthias Fey, Marinka Zitiik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs, 2020.
[27] Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs In ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020), 2020. URL www www.ggraaaaaanng...... io.
[28] Paul D. Dobson and Andrew Doig. Distinguishing enzyme structures from non-enzymes without alignments. Journal of Molecular Biology, 330(4):771-783, July 2003, doi: 10.1016/ s0022-28366 2836((0)006288-44 URL https: org/ /016/s00222 2836 (03)0062-4
[29] Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler- graph kernlls. Journal of Machine Learning Research, 12(77); 2569-2561, 2011. URL http://jmlz wwwpg//apersvvtie http://shraaaahiieeee1.eee /ttppprr///t//sseerrraaiaee..l. .ttl..
[30] I. Schomburg. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Research, 32(90001): ::311D4444 433, January 2004. doi: 10. 10.1093/na//gkh081.. URL https:
/ //doi.org/10. 1093/ nar mar/gkhO8 gkh001
[31] Broto Chakrabarty, Varun Naganathan, Kanak Garg, Yash Agarwal, and Nita Parekh. NAPS update: network analysis of molecular dynamics data and protein-nucleic acid complexes. Nucleic Acids Research, https://doi... org/10..o/.. 1ttp://..1000/a//ggk/////0/
May 2019. doi: 10. 1093/narrgkz339, URL
[32] M.S. Vijayabaskar. GraProStr graphs of protein structures: tool for constructing the graphs and generating graph parameters for protein structures. The Open Bioinformatics Journal, 5(1):53-58, February 2011. doi: 10.114///650062010000000000
URL https:
//doi. /1T44//88766622211000000000
[33] Prabhu Ramachandran and Gael Varoquaux. Mayavi: 3d visualization of scientific data. Computing in Science & Engineering, 13(2):00-5,, March 2011. doi: 10. 0t09/m/cee2211.333 URL htRps//:://dddt org wtt.r///...11///...... 110.//ccemmmmmmmm 2011.35.
[34] Renzo Angles, Mauricio Arenas-Salinas Roberto Gareía, Jose Antonio Reyes-Suarez, and Ehmke Pohl. GSP4pdb: a web tool to visualize, search and explore protein-ligand structural patterns, BMC Bioinformatics, 21(S2), March 2020. doi: 02566/128590002-33322 URL
https://doi.orr//.... 1186/11111111
[35] Roman A. Laskowski and Mark B. Swindells. LigPlot: Multiple ligand- interaction diagrams for drug discovery. Journal of Chemical Information and Modeling, (11))77778-27 October 2011. 1. doi: 10. 10.1021/c220027u. URL https: //doii htt.///ii.ooroo////// 0rg/10. 1021/ ci200227..
[36] Joseph Feinstein, Wentao Shi, J. Ramanujam, and Michal Brylinski. Bionoi: A voronoi diagram-based representation of ligand-binding sites in proteins for machine learning applica- tions. In Methody in Molecular Biology, pages 299-312. Springer US, 2021. doi: 10. 1007/ 978-1-07 1177166-109--5- 1219-_119111 17. URL https: http://iti..ooo//////// 100. 10000797781--110 1-777----716---11 17011-1199--117
[37] Elisabeth Gasteiger, Christine Hoogland, Alexandre Gattiker, Severine Duvaud, Marc R. Wilkins, Ron Appel, and Amos Bairoch. Protein identification and analysis tools on the Ex- PASy server. In The Proteomics Protocols Handbook, pages 571 571-607. Humana Press, 2005, doi: 1385/1 1 1121111-9999990 890- 0:171.
10.1385/1-59259-8900 8090005790 URL htap:::///oi.ooo
[38] Kawashima, P. Pokarowski, M. Pokarowska, A. Kolinski, Katayama, and M. Kanehisa AAindex: amino acid index database, progress report 2008. Nucleic Acids Research, 36 (Database):D2022-2255 December 2007. doi: 10. 10.109////////mm999, URL https: org/ 10. 10.3/99/11 /aa////a///a/// /gkm998.
12

---

[39] Akinori Kidera, Yasuo Konishi, Masahito Oka, Tatsuo Ooi, and Harold A. Scheraga. Statistical analysis of the physical properties of the 20 naturally occurring amino acids. Journal of Protein Chemistry, 4(1):23-55, February 1985. doi: 10.1007/bt01025492. URI. https: 1007 bf0102499..
adwi..org/0... 1007
[40] Jens Meiler, Anita Zeidler, Felix Schmuschke, and Michael Muller. Generation and evaluation of dimension-reduced amino acid parameter representations by artificial neural networks. Journal of Molecular Modeling, 7(9):360- 369, September 2001. https://dol org/10. 10. 1007 00000/000099000038.
doi: 10. 10.1007/s00894410000. URL
[41] Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitmick, Jerry Ma, and Rob Fergus. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences, Proceedings of the National Academy of Sciences, 118(15), 2021. ISSN 0027-8424. doi: 20.10773/naa.2211623333 623911 18.
URL. https: /www///wwwwww/// pnas orgg/content/11///5/e0000
[42] Ehsaneddin Asgari and Mohammad R. K. Mofrad. Continuous distributed representation of biological sequences for deep proteomics and genomies. PLOS ONE, 10(1 10(11):e014128) November 2015, doi: 10. 10.1371/ja) 1.377//jorrll.pooee pon...111.......7 URL httth::///oo org/ 10. 1371/
journal. pone. 0141287.
[43] Wolfgang Kabsch and Christian Sander. Dictionary of protein secondary structure: Pattern recog- nition of hydrogen-bonder and geometrical features. Biopolymers, 22(11):2577-2637 Decem- ber 1983. doi: 10. (000///pp.30022122 URL https://doot http://..o.....0..00000000000110 360.22111...
[44] Yuan Chiang, Wei-Han Hui, and Shu-Wei Chang. Encoding protein dynamic information in graph representation for functional residue identification, 2021.
[45] GetContacts. Getcontacts. URL
github.io//
[46] Bowen Dai and Chris Qailey-Kelloge Protein interaction interface region prediction by geo- metric deep learning. Bioinformaties, March 2021. doi: 10110//boonfommtii//bbbbl...... URL https://doi. org/10. httg///bioinfoommtcccettaaaaaaa
[47] Schrödinger, LLC. The PyMOL molecular graphics system, version 1.8. November 2015.
[48] Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Junction tree variational autoencoder for molecular graph generation, 2018.
[49] Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingho Zhou, Fan Wang, Hua Wu, and Haifeng Wang. Geometry-enhanee enhanced molecular representation learning for property prediction. Nature Machine Intelligence, 4(2): 127-134,, February 2022. doi: 10. 10.1038/44256-011-0004333
URL htthh:t:///ee /ttp::////oiiee http://ooooogg/////// 1038/ s42256- 021-00438-4,
[50] John J. Irwin and Brian Shoichet. ZINC
free database of commercially available compounds for virtual screening. Journal of Chemical Information and Modeling, 45(1): 177-182, December 2004. doi: 10.1021/ci049711 URL https::/ org/10 1021/ ci049714+.
[51] Anna Gaulton, Anne Hersey, Michat Nowotka, A. Patrícia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa Bellis, Elena Cibrián-Uhalte, Mark Davies, Nathan Dedman, Anneli Karlsson, María Paula Magariños, John P. Overington, George Papadatos, Ines Smit, and Andrew R. Leach. The ChEMBL database in 2017. Nucleic Acids Research, 45(D1): November 2016. doi: 10. 10.1093/nar/gkw 1074. URL
https://dois http::///oo..or///// 1ttp://000003////////110000
[52] L. Hofacker, Fontana, F. Stadler, L. S. Bonhoeffer, M. Tacker, and Schuster. Fast folding and comparison of RNA secondary structures. Monatshefte, fr Chemie Chemical Monthly, 127 February 1994. doi: 10. 10.1007/b9000181163 URL https://doo org/10. 10w.:/0000///////0 /f00818163.
13

---

[53] Thomas Gaudelet, Ben Day, Arian R Jamasb. Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B R Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom Blundell, Michael M Bronstein, and Jake P "aaloo--Kinn King. Utilizing graph machine learning within drug discovery and development. Briefings in Bioinformatics, 05 2021. ISSN 1477-4054. doi: 10. 10.1093/bib/bbbb1599 URL https::////// //doii org/10 10.
btbpb/bbbbbbbbb.. b5ab159.
[54] Damian Szklarezyk, Annika L Gable, Katerina C Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T Doncheva, Mare Legeay, Tao Fang. Peer Bork, Lars Jensen, and Christian von Mering. The STRING database in 2021: customizable protein-proten networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research, 49(D1))D605-D611, 11 2020. ISSN 0305-1045 doi: 10. 10.1093/nar/gkaa1000 URL https: / //doi.oorg/1..005/555 www00/09///// http:///ag.......//////aa/aaaa/a gkaa1074.
[55] Rose Oughtred, Jennifer Rust, Christic Chang. Bobby-Joe Breitkreutz, Chris Stark, Andrew Willems, Lorrie Boucher, Genie Leung, Nadine Kolas, Frederick Zhang, Sonam Dolma, Jasmin Coulombe-Huntinggon,, Andrew Chatr-aryamontri, Kara Dolinski, and Mike Tyers. The biogrid database: comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Science, fttts:://onnneeiieni
2021. doi: https://doi.or// org//0. 1tt//00.10022 10.1000//0o.39788 URL www/doiomeeeeeccoocoocoowcwwwooo hts////i//aaaaa/ /abs//b///// 10. pro00/pr.... 3978.
wiley
[56] Heonjong Han, Jac- Won Cho, Sangyoung Lee, Ayoung Yun, Hyojin Kim, Dasom Bac, Sunmo Yang, Chan Yeong Kim, Muyoung Lee, Eunbeen Kim, Sungho Lee, Byunghee Kang, Dabin Jeong, Yaeji Kim, Hyeon-Nae Jeon, Haein Jung, Sunhwee Nam, Michael Chung. Jong-Hoon Kim, and Insuk Lee. TRRUST v2: expanded reference database of human and mouse tran- scriptional regulatory interactions. Nucleic Acids Research, (6(D)) D386, 10 2017. ISSN 0305- 1048. doi: 10. 00.1033/ar/ggxx1133
URL https: http://ooo.orgg////// www.oogg//.... 1093/nar/ wtt0/////////1/......
[57] Zhi-Ping Liu, Canglin Wu, Hongyu Miao, and Hulin Wu. RegNetwork: integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database, 2015, 09 2015. ISSN 1758-0463. doi: 10. 1093/databaseebav005.. URL https://doi. org/ 10. 1093/ database/bavoSS bav095.
[58] Min Zeng, Fuhao Zhang, Fang-Xiang Wu, Yaohang Li, Jianxin Wang, and Min Li. Protein- protein interaction site prediction through combining local and global features with deep neural networks. Bioinformatics, September 2019. doi: 10. 1t.10//oiinfoommtii//booooo http://ooonooomaitaaaaaaaaaeeeea
URL
https://doi.. httt:::/ddi.oor//1...
[59] Arian R. Jamasb, Ben Day, Cätálina Cangea, Pictro Liò, and Tom L. Blundell. Deep learning for Protein Interaction Site Prediction, pages 263-288 Springer US, New York, NY, 2021. ISBN 978 1------1111-44
doi: 10. 1000/7977 1671--11111111160 URL https: 1007 /978- 11199978-11177 111-077716-----1---11111-1------
/
[60] Ben Day, Cätälina Cangea, Arian R. Jamasb, and Pietro Liò. Message passing neural processes, 2020.
[61] T. Amemiya, R. Koike, A. Kidera, and M. Ota. PSCDB: database for protein structural change upon ligand binding. Nucleic Acids Research, 40(D1)) November 2011. doi: 10. /////////////966...... URL https: //doi. org/10. 10. 1093/ htt3//ar//hha 9
[62] Piyush Agrawal, Sumcet Patiyal, Rajesh Kumar, Vinod Kumar, Harinder Singh, Pawan Kumar Raghav, and Gajendra Raghava. ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank. Database, 2019, 01 2019. ISSN 1758-0463. doi: 10. (0.109//attabas//baay/ 142. URL htttp:://dott org/10. 1093/ wwt.:///aaaaae//////ee/ databasee/byy122 bay142. 142.
[63] Aric A. Hagberg, Daniel A. Schult, and Pieter Swart. Exploring network structure, dynamics, and function using networkx. In Gaël Varoquaux, Travis Vaught, and Jarrod Millman, editors, Proceedings of the 7th Python in Science Conference, pages 11 - 15, Pasadena, CA USA, 2008.
[64] Frank Harary and Robert Z. Norman. Some properties of line digraphs. Rendiconti del Circolo Matematico di Palermo, 92))111-166, May 1960. doi: 10. 10.100776008855581. URL https://doi. wtt/:/
14

---

Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] Yes, please see the conclusion (Section 7).
(c) Did you discuss any potential negative societal impacts of your work? [Yes] Please see Supplementary Information E
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes] We have read the ethics review guidelines.
2. If you are including theoretical results...
(a) Did you state the full of assumptions of all theoretical results? [N/A] We are not including theoretical results.
(b) Did you include complete proofs of all theoretical results? [N/A] We are not including theoretical results.
3. If you ran experiments (e.g. for benchmarks)...
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [Yes] Please see the repository: https: / lggggaaapiii... ai
(b) Did you specify all the training details (c.g.. data splits, hyperparameters, how they were chosen)? [Yes] A full description is given in Supplementary Information D
(c) Did you report error bars (e.g.. with respect to the random seed after running experi- ments multiple times)? [Yes] Please see Supplementary.
(d) Did you include the total amount compute and the type of resources used (e.g.. type of GPUs, internal cluster, or cloud provider)? [Yes] Please Supplementary Information D.
4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
(a) your work uses existing assets, did you cite the creators? [Yes] We have cited the appropriate creators in the text.
(b) Did you mention the license of the assets? [Yes] We have described the licensing conditions in the text.
(c) Did you include any new assets either in the supplemental material or as a URL? [Yes] Please see repository: tttt:p://grrahetie ai
(d) Did you discuss whether and how consent was obtained from people whose data you're using/curating? [Yes] We use publicly available data and specify the original licenses.
(e) Did you discuss whether the data you are using/curating contains personally identifiable information offensive content? [N/A] No datasets include personally identifiable information or offensive content.
5. If you used crowdsourcing or conducted research with human subjects...
(a) Did you include the full text of instructions given participants and screenshots, applicable? [N/A] We do not use crowdsourcing or human subjects.
(b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, applicable? [N/A] We do not use crowdsourcing or human subjects.
(c) Did you include the estimated hourly wage paid participants and the total amount spent on participant compensation? [N/A] We do not use crowdsourcing or human subjects.
15


Reviews:
[{"review_id": "k2-sTXvcz-j", "reviewer": "Reviewer_wsXa", "summary": "This work introduces a Python library, Graphein, for transforming raw data including biomolecular structures and interactions from widely used bioinformatics databases into the datasets for geometric deep learning. Graphein supports a broad selection of data sources including small molecules, proteins, RNA, and biological interaction networks. Besides, Graphein provides several machine learning utilities for users to better work with the data.", "questions": "Currently, only a limited number of datasets are constructed in Graphein. Will more datasets that better cover each of the supported data types/sources be designed in the future?", "limitations": "To attract more researchers to use Graphein in their work, the results of baseline models can be collected and provided together with the datasets made by Graphein for comparison.\n", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "- Originality: Although there are existing tools for building biomolecular graphs, they all exhibit limited utilities for geometric deep learning. The proposed tool covers a broader selection of bioinformatics data and provides more utilities for the transformed graph-structured data. \n- Quality: The proposed library provides convenient structure retrieval and graph creation tools. More empirical results based on the library need to be collected and shown.\n- Clarity: The paper is well written and the proposed tool is clearly introduced.\n- Significance: Geometric deep learning in the field of bioinformatics is getting more and more popular these years. Although there are several tools for transforming bioinformatics data to graph-structured data, they only support a small selection of data sources, and the transformed data can only be directly used by limited geometric deep learning libraries. Given that the proposed tool provides wider support for both data sources and downstream libraries, many researchers would be interested in using this tool for preparing graph-structured data for various tasks. \n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "emwLbo9ngy", "reviewer": "Reviewer_qxsn", "summary": "This paper presents \"Graphein\", a tool for the pre-processing and overall data wrangling of biological networks for machine learning tasks.", "questions": "- The documentation seems to be generated from python notebooks, and the output includes a lot of debug information. Could this be made \"toggleable\"? It will improve with initial reads of the reference, as (at a first glance) the output looks like an error.", "limitations": "Considering the quality of Graphein and the existing quality of other well-established pieces of code oriented for computational biology, I consider the limitations in the documentation a minimal issue for Graphein.", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The tool is very well documented and the code seem to be professional-grade. \n\nWhile the documentation in it's current version is very useful, it feels incomplete. This is mitigated by the authors providing a set of easy to follow and high quality set of examples, provided as python notebooks.\n\nThe single \"weakness\" of this package in my opinion is the lack of a manual for the \"advanced API\" (although this reasonable, considering the audience of such and API are expected to understand the inner workings of Graphein to use it adequately).\n\nI've managed to run ALL the examples successfully in a couple of days, and had minimial issues that could be resolved with a quick search on the documentation or reading the next section of the manual. ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "LaQeUSJe6I", "reviewer": "Reviewer_VXGc", "summary": "This paper provides a python library on modeling protein-protein interaction for the field of target discovery and drug design.\nIt includes commonly used database on both structural data and interaction data. Also, for neural network and modeling, it provides interfaces on different scale level for atom level to protein/DNA/RNA level. The code is open-access, which could be helpful for the drug community.", "questions": "- It is well-known that some databases could be extremely large. Could you provides the performance running on large-scale databases.\n\n- do you plan to provide AI baselines and benchmark different methods on the open databases?", "limitations": "- my major concern is the scale and efficiency for this toolbox, especially for large-scale data.", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "- Strengths: this paper gives a brief overview of widely used databases and modeling interface for protein interface and mesh. The python code is open access. This tools could be beneficial for communities from both target discovery and drug design.\n\n- Weaknesses: it could be better if this toolbox could provide common methods and make benchmarks on these standard databases.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "3KG6JLc9-f", "reviewer": "Reviewer_gZ1K", "summary": "Graphein is a library for converting graph information contained in widely used bioinformatics databases to a machine learning friendly form. This allows machine learning researchers consistent and flexible preprocessing steps for structural and interaction graph data. As compared to previous widely used ML benchmarks and libraries which extract and collect fixed benchmark datasets, Graphein allows more customization to add new features as needed. This provides more low-level control for researchers to build and manipulate their own datasets beyond the standard datasets already supplied in tools like pytorch geometric, torch drug, and there are undoubtably researchers who are finding and will find Graphein useful for preparing biological structure and network data.", "questions": "Isn't this paper better suited for the datasets and benchmarks track? This paper is difficult to evaluate on the typical dimensions of NeurIPS main track: originality, quality, clarity, and significance. \n\nConsistent and repeatable datasets with train and test splits have proven extremely useful in the broader ML field. Using these benchmarks, methods can be directly and easily compared with minimal task-specific knowledge. These benchmarks are relatively easy to standardize and agree upon in image and natural language tasks. However, in biology there are vastly greater possible tasks and more heterogenous data sources. Graphein provides a very flexible pre-processing pipeline. I wonder if the authors have any thoughts as to how to ensure consistency between as data sources and data pipelines change? \n\n\nMinor comments:\nLine 332: pre-rocessing  --> pre-processing\n\n----------------------------\nPost Response update:\n\nThank you to the authors for your detailed response. My concerns have been addressed and I have updated my score accordingly 4 --> 6. I believe additional discussion comparing Graphein to other molecule processing libraries would be helpful to answer the question \"Why Graphein?\" for ML practitioners, but overall a good contribution to ML infrastructure for biological graph dataset processing.", "limitations": "Yes", "rating": 6, "confidence": 2, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n* Flexible preprocessing pipeline that abstracts away some of the implementation details of bioinformatics datasets.\n* Excellent and clearly written documentation and notebooks giving a high-level introduction to the library.\n* Good visualization tools, particularly for protein structure graphs at various levels of granularity.\n* Easy to install and provides docker containers for portability. \n\t\nWeaknesses:\n* The paper is mostly a summary of what the library is. I’m not sure I would ever recommend reading the paper over the documentation (of course this means the documentation is already excellent).\n* There is limited comparison to the features provided in other related libraries. There are a number of tools that load and process geometric datasets, this paper could provide more information as to where Graphein fits in this larger ecosystem, what features it provides that other libraries do not, and why someone should use Graphein rather over custom preprocessing steps.\n* There is no use / validation of datasets provided in the paper so it is difficult to evaluate how useful these particular datasets are in benchmarking methods. \n* As this paper presents a python library for curating, visualizing, and preprocessing datasets, there is nothing novel in the traditional ML sense of new ideas or architectures to evaluate in this work.\n\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept