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The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. Tyler W. ChemMine Tools is an online service for small molecule data analysis. It provides a web interface to a set of cheminformatics and data mining tools that are useful for various analysis routines performed in chemical genomics and drug discovery. The service also offers programmable access options via the R library ChemmineR. The primary functionalities of ChemMine Tools fall into five major application areas: data visualization, structure comparisons, similarity searching, compound clustering and prediction of chemical properties. First, users can upload compound data sets to the online Compound Workbench. Numerous utilities are provided for compound viewing, structure drawing and format interconversion. Second, pairwise structural similarities among compounds can be quantified. Third, interfaces to ultra-fast structure similarity search algorithms are available to efficiently mine the chemical space in the public domain. Fourth, the service includes a Clustering Toolbox that integrates cheminformatic algorithms with data mining utilities to enable systematic structure and activity based analyses of custom compound sets. Fifth, physicochemical property descriptors of custom compound sets can be calculated. These descriptors are important for assessing the bioactivity profile of compounds in silico and quantitative structure—activity relationship QSAR analyses. Cheminformatics tools for analyzing small molecule screening data play an important role in many fields including chemical biology, chemical genomics, drug discovery and agrochemical research 1—3. Informatics resources in these areas are essential for exploring the structure, properties and bioactivity of biologically relevant molecules. To provide these capabilities, software tools are required for analyzing the structural similarities, physicochemical properties and bioactivity profiles of natural and synthetic compounds to gain insight into their modes of action in biological systems. This information is important for the development of effective small molecule probes for studying the functions of protein and cellular networks in chemical genomics and drug discovery research 4. In addition, similar informatics resources are required for identifying the structural and physicochemical relationships among compounds from metabolic or signaling pathways 5—7. The rapidly growing relevance of chemical genomics approaches for modern biology research has significantly increased demand for small molecule mining systems in academia 8. Currently, the structures of over 30 million distinct small molecules are available in open-access databases, including PubChem , ChemBank and many others 9— In addition, preliminary bioactivity data from hundreds of high-throughput screening HTS experiments against a wide spectrum of target sites have become available for almost one million compounds in the bioassay sections of various public databases see below; 9 , 10 , 15 , To efficiently analyze these resources, the development of novel compound data mining and cheminformatic web services is essential. While there has been extensive development of public domain small molecule databases in recent years 6 , 9—11 , 13—24 , the number of open access web services for analyzing public or custom small molecule data is extremely limited at this point 25 , Thus far, most development has been focused on standalone software applications targeted toward computational rather than experimental scientists. Examples of software designed for non-expert users in this field are Chembench 33 for online quantitative structure—activity relationship QSAR modeling and KNIME 34 for designing data analysis pipelines. Here, we present ChemMine Tools as an online portal to a variety of cheminformatics, visualization, search and clustering tools for small molecule data. The utilities provided by this service are useful for various analysis and data mining routines of small molecule screening experiments in chemical genomics and related areas. An easy to use web interface makes these tools accessible to experimental scientists without an extensive computational background. Conceptually, the ChemMine Tools online service is divided into five application domains Figure 1 and Table 1 : i a Compound workbench for data imports and result management; ii a Structure Similarity toolbox to quantify the similarities among compounds; iii a Search toolbox for retrieving similar compounds from PubChem; iv a Clustering toolbox for accessing clustering and data visualization tools; and v a Property toolbox for predicting physicochemical properties of compounds. Currently, the server integrates over 30 cheminformatics and data mining tools that were developed by this or related open source projects. The modular organization of the ChemMine Tools service has several advantages. For instance, it maximizes the transparency and maintainability of the system, and simplifies the addition of new features and analysis methods upon user request. The web interface of ChemMine Tools is written in Python using the object-oriented and highly scalable Django web framework. Moreover, the ChemMine Tools project is dedicated to an open access and resource sharing policy. All of its online services and downloadable software components are freely available without restrictions. The following subsections give a detailed description of the underlying algorithms and software tools used by the individual ChemMine Tools services. Illustration of the functionalities provided by ChemMine Tools. The utilities of the five application domains i—v are listed in more detail in Table 1. Acronyms defined in text. A central feature of ChemMine Tools is its Compound workbench. It provides a flexible online workspace to upload, manage and visualize small molecule data. Compounds can be imported by reading them from local files, copy and paste, PubChem queries see Search toolbox or by interacting with the service through the ChemmineR library 35 within the statistical programming environment R. The latter is an extension of the ChemMine Tools project to provide a programmable interface to more advanced users. Alternatively, compounds can be drawn online with the JME Molecular Editor 36 and then added to the Compound workbench. After the import, one can organize and annotate the compounds or view their structure images in single or batch modes. These images are generated in real time from the underlying structure definition data using the structure depiction tool of the CACTVS software suite 11 which runs on the server side. To revisit instances of compound sets, users can save their workbench for later use by downloading the compounds to local files. Once the user has populated the Compound workbench with structures, it serves as a central submission system to all downstream analysis services. In many small molecule screening data analysis routines it is important to compute objective similarity measures among compounds as a means to compare and prioritize structurally related lead compounds. To provide this functionality, ChemMine Tools has implemented two algorithms for computing similarity coefficients among compound structures. The first employs atom pairs as structural descriptors 37 and the widely used Tanimoto coefficient as a similarity measure see below for more details. Alternatively, users can choose other similarity coefficients, such as Tversky or Dice The second algorithm identifies the maximum common substructure MCS shared among compound pairs Subsequently, the size of both compounds and the size of their shared MCS is used to calculate the available similarity coefficients. The underlying MCS algorithm often provides the most accurate and sensitive similarity measure, especially for compounds with large size differences 40 , To efficiently mine much of the chemical structure and bioactivity space available in the public domain, the ChemMine Tools service provides text and structure similarity search methods that interface with the PubChem database 15 via its SOAP -based Power User Gateway PUG data exchange feature. During an analysis session, instantaneous search functionality is often important for retrieval of detailed property and annotation information for compounds of interest, or to identify related structures. When the fingerprint method is chosen, the query is sent to PubChem , where the structure search is performed and the results are returned to the compound workbench. These two tools possess complementary strengths and weaknesses in identifying weak similarities among compounds Clustering of compounds by structural or property similarity can be a powerful approach to correlating compound features with biological activity. Clustering tools are also widely utilized for diversity analyses to identify structural redundancies and other biases in compound libraries. ChemMine Tools' clustering workbench provides an online interface to three clustering algorithms which include hierarchical clustering, multidimensional scaling MDS and binning clustering The following provides a short overview of these tools, while a more detailed outline of the underlying theory and clustering schemes is available in the online tutorial. When clustering by structural similarity, the required similarity measures are computed by first generating the atom pair descriptors features for each compound which are then used to calculate a similarity matrix based on the common and unique features observed among all compound pairs using the Tanimoto coefficient. The Tanimoto coefficient has a range from 0 to 1 with higher values indicating greater similarity than lower ones. For the subsequent clustering steps, the similarity matrix is converted into a distance matrix by subtracting the similarity values from 1. These three programs complement one another with respect to their data outputs and visualization options. Hierarchical clustering organizes compounds by similarity in a tree with branch lengths proportional to the item-to-item compound-to-compound similarities, while the MDS output encodes this information in a scatter plot. These two methods do not directly provide assignments of compounds to discrete similarity groups; assignments are generated downstream of the actual clustering process using various post-processing methods, such as tree cutting approaches. The binning clustering output provides these groupings directly for a user-definable similarity cutoff. For instance, if a Tanimoto coefficient of 0. Final results are presented as interactive visualization pages to simplify the interpretation of the often complex clustering results. The hierarchical clustering result page uses the Google Maps API to generate zoom- and click-able trees aligned with molecular structure images. Moreover, heat maps of user uploaded data containing compound property, activity or other information can be viewed alongside the tree. A similar system is used to present the MDS results as click-able scatter plots with cursor-over viewing of compound structures. The binning clustering results are presented in a table view containing among other information the cluster identifiers and the corresponding compound depictions. They are also useful for enriching compound collections with desirable properties. Physicochemical property data are essential for predicting bioactive and other properties of small molecules using modern machine learning approaches. These data are fundamental to the development of QSAR models This service can calculate 38 physicochemical property values, including Lipinski descriptors for custom compound sets. The resulting property tables can be downloaded or further processed on ChemMine Tools by sending them to the Clustering toolbox. There, they can be used to cluster compounds by similar property profiles, as described above, or the data can be visualized as a heat map next to the hierarchical clustering trees. ChemMine Tools is an online service for compound analysis in the chemical genomics field. The service is unique in that it integrates a large number of cheminformatic programs with clustering and visualization functionalities. Additional outstanding features of ChemMine Tools include: i its commitment to publicly developed open source software throughout its infrastructure; ii its strong dedication to the development of new cheminformatic tools and their free distribution in the community; and iii the integration of its many components into a unified online and downloadable software infrastructure which maximizes their utility for diverse tasks with different levels of complexity and customization needs. An intuitive web interface makes these tools accessible to scientists with limited computational background, while simultaneously providing a programmable interface for advanced users. To the best of our knowledge, there are currently no related online services available that provide a comparable suite of functionalities. Overlaps exist, however they are limited to isolated functionalities. For instance, ChemDB and VCCLab 13 , 43 can be used for property predictions and structure format interconversions of single compound queries; and PubChem supports structure-based clustering for compounds retrieved from its own database. In the future, many additional utilities will be added to the ChemMine Tools service including the addition of MCS-based search functionality within the Similarity toolbox to support more complex graph-based search strategies against custom compound sets imported into the Compound workbench. Existing functionalities for analyzing bioactivity data will also be expanded by adding a Bioactivity toolbox that will contain regression, machine learning and QSAR modeling tools. Additionally, we thank our systems administrator Aleksandr Levchuk for assistance in debugging these tools, and expertly maintaining the necessary computational resources. Google Scholar. Google Preview. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. Sign In or Create an Account. Sign in through your institution. NAR Journals. Advanced Search. Search Menu. Article Navigation. Close mobile search navigation Article Navigation. Volume Article Contents Abstract. Journal Article. ChemMine tools: an online service for analyzing and clustering small molecules. Backman , Tyler W. Oxford Academic. Yiqun Cao. Revision received:. Cite Cite Tyler W. Select Format Select format. Permissions Icon Permissions. Abstract ChemMine Tools is an online service for small molecule data analysis. Figure 1. Open in new tab Download slide. Table 1. Open in new tab. List of services provided by ChemMine Tools. The names of software tools, libraries and environments are italicized. From knowing to controlling: a path from genomics to drugs using small molecule probes. Google Scholar Crossref. Search ADS. Google Scholar PubMed. ChemBank: a small-molecule screening and cheminformatics resource database. ZINC—a free database of commercially available compounds for virtual screening. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Comparison of the NCI open database with seven large chemical structural databases. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. ChEBI: a database and ontology for chemical entities of biological interest. WENDI: a tool for finding non-obvious relationships between compounds and biological properties, genes, diseases and scholarly publications. Recent developments of the chemistry development kit CDK - an open-source java library for chemo- and bioinformatics. Chemical descriptors library CDL : a generic, open source software library for chemical informatics. Feature selection for descriptor based classification models. Human intestinal absorption HIA. Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients. Analysis and display of the size dependence of chemical similarity coefficients. A maximum common substructure-based algorithm for searching and predicting drug-like compounds. Maximum common subgraph isomorphism algorithms for the matching of chemical structures. Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. Accelerated similarity searching and clustering of large compound sets by geometric embedding and locality sensitive hashing. Managing, profiling and analyzing a library of 2. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Published by Oxford University Press. Issue Section:. Download all slides. Comments 0. Add comment Close comment form modal. I agree to the terms and conditions. You must accept the terms and conditions. Add comment Cancel. Submit a comment. Comment title. You have entered an invalid code. Submit Cancel. Thank you for submitting a comment on this article. Your comment will be reviewed and published at the journal's discretion. Please check for further notifications by email. Views 9, More metrics information. Total Views 9, Email alerts Article activity alert. Advance article alerts. New issue alert. Subject alert. Receive exclusive offers and updates from Oxford Academic. Citing articles via Web of Science Latest Most Read Most Cited A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity. MAPbrain: a multi-omics atlas of the primate brain. MethyLasso: a segmentation approach to analyze DNA methylation patterns and identify differentially methylated regions from whole-genome datasets. More from Oxford Academic. Science and Mathematics. Authoring Open access Purchasing Institutional account management Rights and permissions. Get help with access Accessibility Contact us Advertising Media enquiries. ChemmineR a. Atom Pairs a. MCS a. EI Search a.

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