Subgraph Isomorphism Research Articles

Knowledge graph based System model configuration design

When solving the complexity problem between different fields, traditional document-based system engineering (TSE) is difficult to meet current research and development needs, and model-based system engineering (MBSE) is the best choice. With the implementation and application of MBSE in enterprises, the system model files are gradually accumulated. Therefore, this paper studies the reusability of the system model and builds the system model knowledge graph to achieve efficient reuse of the existing system model. A product configuration method based on subgraph matching is proposed and by configuring the product, the designer can obtain more feasible solutions, and then get the best design solution. Finally, an example of remote sensing satellite design is given to verify the effectiveness of the method.

Isomorphic bisections of cubic graphs

Graph partitioning, or the dividing of a graph into two or more parts based on certain conditions, arises naturally throughout discrete mathematics, and problems of this kind have been studied extensively. In the 1990s, Ando conjectured that the vertices of every cubic graph can be partitioned into two parts that induce isomorphic subgraphs. Using probabilistic methods together with delicate recolouring arguments, we prove Ando's conjecture for large connected graphs.

Ligand-based design approach of potential Bcl-2 inhibitors for cancer chemotherapy

Background and ObjectivesOverexpression of prosurvival Bcl-2 family members make tumor cells resistant to conventional cancer therapeutic agents. It is commonly observed feature in many different types of human tumors. Hence, small-molecules as Bcl-2 inhibitors may have a promising therapeutic potential for the treatment of human cancer. The given study focusses on development of novel and small Bcl-2 inhibitors using ligand-based drug design approach. MethodsLigand based pharmacophore was generated using the PHASE tool of Schrödinger and screened ZINC database through ZINCPharmer webserver to identify compounds with similar features. Compounds having good fitness score were selected for molecular docking and binding interactions were compared with drugs in market as well as trials. QSAR model was generated using advanced AutoQSAR tool and validated for prediction of unknown compounds. QSAR prediction of in silico active identified three potential compounds and were subjected to investigate stability by molecular dynamics simulations and MM-PBSA binding energy calculations. ResultsStudy identified three in silico potential molecules with good stability and binding affinity. Further substructure search and pIC50 value prediction has identified six more molecules. Total nine molecules have demonstrated good drug likeness features. ConclusionFinal oral rat LD50 calculation of nine molecules has identified three hit molecules i.e., ZINC76760927, ZINC76768675 and ZINC52767796 for further in vitro and in vivo testing as safe and potential Bcl-2 inhibitors.

Ambit-SLN: an Open Source Software Library for Processing of Chemical Objects via SLN Linear Notation.

SLN (SYBYL Line Notation) is the most comprehensive and rich linear notation for representation of chemical objects of various kinds facilitating a wide range of cheminformatics algorithms. Though, it is not the most popular linear notation nowadays, SLN has capabilities for supporting the most challenging tasks of the present day cheminformatics research. We present Ambit-SLN, a new software library for cheminformatics processing of chemical objects via linear notation SLN. Ambit-SLN is developed as a part of the cheminformatics platform AMBIT. It is an open-source tool, distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit-SLN includes a parser for the full SLN syntax of chemical structures and substructure search queries including support for macro and Markush atoms, global and local dictionaries and user defined properties which can be stored and used by the Ambit data model. The Ambit-SLN library includes functionalities for substructure matching based on SLN query strings and utilities for conversion of SLN objects to other chemical formats such as SMILES and SMARTS. The functionality for Markush atom expansion can be used for generation of combinatorial structure sets.

Exploring Chemical Information in PubChem.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database that serves scientific communities as well as the general public. This database collects chemical information from hundreds of data sources and organizes them into multiple data collections, including Substance, Compound, BioAssay, Protein, Gene, Pathway, and Patent. These collections are interlinked with each other, allowing users to discover related records in the various collections (e.g., drugs targeting a protein or genes modulated by a chemical). PubChem can be searched by keyword (e.g., a chemical, protein, or gene name) as well as by chemical structure. The input structure can be provided using popular line notations or drawn with the PubChem Sketcher. PubChem supports various types of structure searches, including identity search, 2‐D and 3‐D similarity searches, and substructure and superstructure searches. Results from multiple searches can be combined using Boolean operators (i.e., AND, OR, and NOT) to formulate complex queries. PubChem allows the user to quickly retrieve a list of records annotated with a particular classification or ontological term. This paper provides step‐by‐step instructions on how to explore PubChem data with examples of commonly requested tasks. © 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Finding genes and proteins that interact with a given compoundBasic Protocol 2: Finding drug‐like compounds similar to a query compound through a two‐dimensional (2‐D) similarity searchBasic Protocol 3: Finding compounds similar to a query compound through a three‐dimensional (3‐D) similarity searchSupport Protocol: Computing similarity scores between compoundsBasic Protocol 4: Getting the bioactivity data for the hit compounds from substructure searchBasic Protocol 5: Finding drugs that target a particular geneBasic Protocol 6: Getting bioactivity data of all chemicals tested against a protein.Basic Protocol 7: Finding compounds annotated with classifications or ontological termsBasic Protocol 8: Finding stereoisomers and isotopomers of a compound through identity search

Bayesian forecasts for dark matter substructure searches with mock pulsar timing data

Dark matter substructure, such as primordial black holes (PBHs) and axion miniclusters, can induce phase shifts in pulsar timing arrays (PTAs) measurements due to gravitational effects. In order to gain a more realistic forecast for the detectability of such models of dark matter with PTAs, we propose a Bayesian inference framework to search for phase shifts generated by PBHs and perform the analysis on mock PTA data. For most PBH masses the constraints on the dark matter abundance agree with previous (frequentist) analyses (without mock data) to \U0001d4aa(1) factors. This further motivates a dedicated search for PBHs (and dense small scale structures) in the mass range from 10-8 M ☉ to well above 102 M ☉ with the Square Kilometer Array. Moreover, with a more optimistic set of timing parameters, future PTAs are predicted to constrain PBHs down to 10-11 M ☉. Lastly, we discuss the impact of backgrounds, such as Supermassive Black Hole Mergers, on detection prospects, suggesting a future program to separate a dark matter signal from other astrophysical sources.

Fragment-based screening and hit-based substructure search: Rapid discovery of 8-hydroxyquinoline-7-carboxylic acid as a low-cytotoxic, nanomolar metallo β-lactamase inhibitor.

Metallo-β-lactamases (MBLs) are zinc-containing carbapenemases that inactivate a broad range of β-lactam antibiotics. There is a lack of β-lactamase inhibitors for restoring existing β-lactam antibiotics arsenals against common bacterial infections. Fragment-based screening of a non-specific metal chelator library demonstrates 8-hydroxyquinoline as a broad-spectrum nanomolar inhibitor against VIM-2 and NDM-1. A hit-based substructure search provided an early structure-activity relationship of 8-hydroxyquinolines and identified 8-hydroxyquinoline-7-carboxylic acid as a low-cytotoxic β-lactamase inhibitor that can restore β-lactam activity against VIM-2-expressing E.coli. Molecular modeling further shed structural insight into its potential mode of binding within the dinuclear zinc active site. 8-Hydroxyquinoline-7-carboxylic acid is highly stable in human plasma and human liver microsomal study, making it an ideal lead candidate for further development.

IP-cores watermarking scheme at behavioral level using genetic algorithms

This paper presents an improved watermarking scheme for soft Intellectual Property (IP) -Cores using Genetic Algorithms (GAs). For this purpose, a watermark signature and an IP-Core behavioral description are translated into Finite State Machines (FSMs). Both FSMs are merged in a single one containing a watermarked IP-Core without disrupting its original functionality. Therefore, we tackle NP-completeness of the subgraph isomorphism problem found during FSMs merging process via a tailored GA. However, not deeply embedded states are a key problem that may ease watermark’s removal. To overcome this issue, a FSM reduction algorithm is also considered in the proposed watermarking scheme. Moreover, the proposed scheme also targets transitions regrouping which impact the amount of hardware resources usage and a mechanism for selecting the best watermarked FSM. A thorough empirical assessment shows a significant improvement in terms of reduction and watermark embedding strength.

Subgraph matching on temporal graphs

Temporal graphs are graphs whose edges are associated with timestamps. Subgraph matching on temporal graphs retrieves temporal subgraphs whose edge timestamps satisfy user-specified temporal orders. In this paper, a temporal query pattern is composed of a query graph with an arbitrary structure and a partial order on the edge set of the query graph. Moreover, the time span between the minimum and the maximum edge timestamps in a matching is required to be less than or equal to a specified threshold. This paper proposes a temporal subgraph matching algorithm based on two key techniques. First, a memory-efficient index structure called TO-tree is designed to compactly store all necessary information required for finding all temporal subgraph matchings. The TO-tree constructed for a temporal query pattern is much smaller than the temporal graph because unnecessary information is mostly excluded from the TO-tree by three powerful filters. The second technique is a temporal subgraph matching enumeration method that runs on the TO-tree instead of on the temporal graph. This enumeration method expands temporal subgraph matchings in an edge-by-edge manner. Since the TO-tree can fit into the main memory, the enumeration method runs very fast on the TO-tree. An extensive experimental evaluation has been carried out. The experimental results show that the TO-tree index structure is memory-efficient, which in turn enables a fast temporal subgraph matching enumeration. Overall, our algorithm is at least 3X faster than the baseline algorithm adapted from the state-of-the-art non-temporal subgraph matching algorithm CECI and is at least 4X faster than the temporal subgraph matching algorithm HASSE.

TemporalRI: subgraph isomorphism in temporal networks with multiple contacts

Temporal networks are graphs where each edge is associated with a timestamp denoting when two nodes interact. Temporal Subgraph Isomorphism (TSI) aims at retrieving all the subgraphs of a temporal network (called target) matching a smaller temporal network (called query), such that matched target edges appear in the same chronological order of corresponding query edges. Few algorithms have been proposed to solve the TSI problem (or variants of it) and most of them are applicable only to small or specific queries. In this paper we present TemporalRI, a new subgraph isomorphism algorithm for temporal networks with multiple contacts between nodes, which is inspired by RI algorithm. TemporalRI introduces the notion of temporal flows and uses them to filter the search space of candidate nodes for the matching. Our algorithm can handle queries of any size and any topology. Experiments on real networks of different sizes show that TemporalRI is very efficient compared to the state-of-the-art, especially for large queries and targets.

GraphMineSuite

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by >2X), maximal clique listing (by >9×),k-clique listing (by up to 1.1×), and subgraph isomorphism (by 2.5×), also obtaining better theoretical performance bounds.

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases.

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.

Web Service Discovery Using Bio-Inspired Holistic Matching Based Linked Data Clustering Model for RDF Data

Introduction: Resource Description Framework (RDF) is the de-facto standard language model for semantic data representation on semantic web. Designing an efficient management of RDF data with huge volume and efficient querying techniques are the primary research areas in semantic web. Methods: So far, several RDF management methods have been offered with data storage designs and query processing algorithms for data retrieval. However, these methods do not adequately address the presence of irrelevant links that degrade the performance of web service discovery. In this paper, we propose a Bio-inspired Holistic Matching based Linked Data Clustering (BHM-LDC) technique for efficient management and querying of RDF data. This technique is essentially based on three algorithms which are designed for RDF data storing, clustering the linked data and web service discovery respectively. Initially, the BHM-LDC technique store the RDF dataset as graph based linked data. Results and Discussion: Then, an Integrated Holistic Entity Matching based Distributed Genetic Algorithm (IHEM-DGA) is proposed to cluster the linked data. Finally, a sub-graph matching based web Service discovery Algorithm that uses the clustered triples has been proposed to find the best web services. Our experimental results reveal the performance of the proposed web service discovery approach by applying on business RDF dataset.

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

Chemical similarity methods for analyzing secondary metabolite structures

Abstract The relation that exists between the structure of a compound and its function is an integral part of chemoinformatics. The similarity principle states that “structurally similar molecules tend to have similar properties and similar molecules exert similar biological activities”. The similarity of the molecules can either be studied at the structure level or at the descriptor level (properties level). Generally, the objective of chemical similarity measures is to enhance prediction of the biological activities of molecules. In this article, an overview of various methods used to compare the similarity between metabolite structures has been provided, including two-dimensional (2D) and three-dimensional (3D) approaches. The focus has been on methods description; e.g. fingerprint-based similarity in which the molecules under study are first fragmented and their fingerprints are computed, 2D structural similarity by comparing the Tanimoto coefficients and Euclidean distances, as well as the use of physiochemical properties descriptor-based similarity methods. The similarity between molecules could also be measured by using data mining (clustering) techniques, e.g. by using virtual screening (VS)-based similarity methods. In this approach, the molecules with the desired descriptors or /and structures are screened from large databases. Lastly, SMILES-based chemical similarity search is an important method for studying the exact structure search, substructure search and also descriptor similarity. The use of a particular method depends upon the requirements of the researcher.

Discovery of network motifs based on induced subgraphs using a dynamic expansion tree

Biological networks are powerful representations of topological features in biological systems. Finding network motifs in biological networks is a computationally hard problem due to their huge size and abrupt increase of search space with the increase of motif size. Motivated by the computational challenges of network motif discovery and considering the importance of this topic, an efficient and scalable network motif discovery algorithm based on induced subgraphs in a dynamic expansion tree is proposed. This algorithm uses a pruning strategy to overcome the space limitation of the static expansion tree. The proposed algorithm can identify large network motifs up to size 15 by significantly reducing the computationally expensive subgraph isomorphism checks. Further, the present work avoids the unnecessary growth of patterns that do not have any statistical significance. The runtime performance of the proposed algorithm outperforms most of the existing algorithms for large network motifs.

GraphZero

Subgraph matching is a fundamental task in many applications which identifies all the embeddings of a query pattern in an input graph. Compilation-based subgraph matching systems generate specialized implementations for the provided patterns and often substantially outperform other systems. However, the generated code causes significant computation redundancy and the compilation process incurs too much overhead to be used online, both due to the inherent symmetry in the structure of the query pattern. In this paper, we propose an optimizing query compiler, named GraphZero, to completely address these limitations through symmetry breaking based on group theory. GraphZero implements three novel techniques. First, its schedule explorer efficiently prunes the schedule space without missing any high-performance schedule. Second, it automatically generates and enforces a set of restrictions to eliminate computation redundancy. Third, it generalizes orientation, a surprisingly effective optimization that was only used for clique patterns, to apply to arbitrary patterns. Evaluation on multiple query patterns shows that GraphZero outperforms two state-of-the-art compilation and non-compilation based systems by up to 40X and 2654X, respectively.

Efficient access methods for very large distributed graph databases

Subgraph searching is an essential problem in graph databases, but it is also challenging due to the involved subgraph isomorphism NP-Complete sub-problem. Filter-Then-Verify (FTV) methods mitigate performance overheads by using an index to prune out graphs that do not fit the query in a filtering stage, reducing the number of subgraph isomorphism evaluations in a subsequent verification stage. Subgraph searching has to be applied to very large databases (tens of millions of graphs) in real applications such as molecular substructure searching. Previous surveys have identified the FTV solutions GraphGrepSX (GGSX) and CT-Index as the best ones for large databases (thousands of graphs), however they cannot reach reasonable performance on very large ones (tens of millions graphs). This paper proposes a generic approach for the distributed implementation of FTV solutions. Besides, three previous methods that improve the performance of GGSX and CT-Index are adapted to be executed in clusters. The evaluation shows how the achieved solutions provide a great performance improvement (between 70% and 90% of filtering time reduction) in a centralized configuration and how they may be used to achieve efficient subgraph searching over very large databases in cluster configurations.

IDSM ChemWebRDF: SPARQLing small-molecule datasets

The Resource Description Framework (RDF), together with well-defined ontologies, significantly increases data interoperability and usability. The SPARQL query language was introduced to retrieve requested RDF data and to explore links between them. Among other useful features, SPARQL supports federated queries that combine multiple independent data source endpoints. This allows users to obtain insights that are not possible using only a single data source. Owing to all of these useful features, many biological and chemical databases present their data in RDF, and support SPARQL querying. In our project, we primary focused on PubChem, ChEMBL and ChEBI small-molecule datasets. These datasets are already being exported to RDF by their creators. However, none of them has an official and currently supported SPARQL endpoint. This omission makes it difficult to construct complex or federated queries that could access all of the datasets, thus underutilising the main advantage of the availability of RDF data. Our goal is to address this gap by integrating the datasets into one database called the Integrated Database of Small Molecules (IDSM) that will be accessible through a SPARQL endpoint. Beyond that, we will also focus on increasing mutual interoperability of the datasets. To realise the endpoint, we decided to implement an in-house developed SPARQL engine based on the PostgreSQL relational database for data storage. In our approach, data are stored in the traditional relational form, and the SPARQL engine translates incoming SPARQL queries into equivalent SQL queries. An important feature of the engine is that it optimises the resulting SQL queries. Together with optimisations performed by PostgreSQL, this allows efficient evaluations of SPARQL queries. The endpoint provides not only querying in the dataset, but also the compound substructure and similarity search supported by our Sachem project. Although the endpoint is accessible from an internet browser, it is mainly intended to be used for programmatic access by other services, for example as a part of federated queries. For regular users, we offer a rich web application called ChemWebRDF using the endpoint. The application is publicly available at https://idsm.elixir-czech.cz/chemweb/.

PDBcle – An online tool for extracting chain structure and sequence of macromolecule and small molecule structure from the Protein Data Bank

Background: Protein Data Bank (PDB) is the most popular structure database that contains experimentally determined three-dimensional (3D) structures of biological macromolecules and small molecules. The rich features of PDB are keyword assisted advanced text search, structure search by sequence alignment, sequence motif search, ligand to target-ligand complex search through SMILES substructure search, JSON API query search, structure alignment, structure quality assessment, genome viewer, and 3D structure viewer. It is widely used in molecular modelling and computer-aided drug design. PDBcle is a simple tool to extract chain sequence of protein/nucleotide and 3D structure of protein/ nucleotide/ ligand from the PDB. Objectives: To construct an online tool for separating molecule-wise chain sequence and structure of polymers and non-polymer structures in a macromolecule. Moreover, the separated sequences and structures are produced to moleculespecific standard file format. Methods: The graphical web-interface of PDBcle tool has been designed using PHP, CSS, and pure JavaScript. Parsing the atomic coordinate records and sequence records from the PDBML/XML file and/or PDBx/mmJSON file through the API of PDB was done through PHP server script. Findings: The PDBcle tool retrieves and generates separate structure/sequence files for each amino acid/RNA chain, and pair of chains for DNA base pairs with/without ligand complex from the PDB. The ligand molecules are separated and sorted from the chains and produced to an SDF file. Applications: PDBcle tool is publicly accessible at https://www.biogem.org/tool/pdbcle/. Keywords: PDBcle; PDB; PDB Chain and Ligand Extractor; PDB Sequence Extractor; PDBML