http://www.jcheminf.com The latest research articles published by Journal of Cheminformatics 2010-08-31T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way. http://www.jcheminf.com/content/2/1/7 Barry Hardy Nicki Douglas Christoph Helma Micha Rautenberg Nina Jeliazkova Vedrin Jeliazkov Ivelina Nikolova Romualdo Benigni Olga Tcheremenskaia Stefan Kramer Tobias Girschick Fabian Buchwald Joerg Wicker Andreas Karwath Martin Gutlein Andreas Maunz Haralambos Sarimveis Georgia Melagraki Antreas Afantitis Pantelis Sopasakis David Gallagher Vladimir Poroikov Dmitry Filimonov Alexey Zakharov Alexey Lagunin Tatyana Gloriozova Sergey Novikov Natalia Skvortsova Dmitry Druzhilovsky Sunil Chawla Indira Ghosh Surajit Ray Hitesh Patel Sylvia Escher Journal of Cheminformatics 2010, 2:7 2010-08-31T00:00:00Z doi:10.1186/1758-2946-2-7 Journal of Cheminformatics 1758-2946 2 7 2010-08-31T00:00:00Z PDF Background: In recent years, there has been a huge increase in the amount of publicly-available and proprietary information pertinent to drug discovery. However, there is a distinct lack of data mining tools available to harness this information, and in particular for knowledge discovery across multiple information sources. At Indiana University we have an ongoing project with Eli Lilly to develop web-service based tools for integrative mining of chemical and biological information. In this paper, we report on the first of these tools, called WENDI (Web Engine for Non-obvious Drug Information) that attempts to find non-obvious relationships between a query compound and scholarly publications, biological properties, genes and diseases using multiple information sources. Results: We have created an aggregate web service that takes a query compound as input, calls multiple web services for computation and database search, and returns an XML file that aggregates this information. We have also developed a client application that provides an easy-to-use interface to this web service. Both the service and client are publicly available. Conclusions: Initial testing indicates this tool is useful in identifying potential biological applications of compounds that are not obvious, and in identifying corroborating and conflicting information from multiple sources. We encourage feedback on the tool to help us refine it further. We are now developing further tools based on this model. http://www.jcheminf.com/content/2/1/6 Qian Zhu Michael Lajiness Ying Ding David Wild Journal of Cheminformatics 2010, 2:6 2010-08-20T00:00:00Z doi:10.1186/1758-2946-2-6 Journal of Cheminformatics 1758-2946 2 6 2010-08-20T00:00:00Z XML Background: QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. Results: We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Conclusions: Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but also allows for analyzing the effect descriptors have on the statistical model's performance. The presented Bioclipse plugins equip scientists with graphical tools that make QSAR-ML easily accessible for the community. http://www.jcheminf.com/content/2/1/5 Ola Spjuth Egon Willighagen Rajarshi Guha Martin Eklund Jarl Wikberg Journal of Cheminformatics 2010, 2:5 2010-06-30T00:00:00Z doi:10.1186/1758-2946-2-5 Journal of Cheminformatics 1758-2946 2 5 2010-06-30T00:00:00Z XML No description available http://www.jcheminf.com/content/2/1/4 Kristina Hettne Antony Williams Erik van Mulligen Jos Kleinjans Valery Tkachenko Jan Kors Journal of Cheminformatics 2010, 2:4 2010-06-03T00:00:00Z doi:10.1186/1758-2946-2-4 Journal of Cheminformatics 1758-2946 2 4 2010-06-03T00:00:00Z XML Background: Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. Results: We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. Conclusions: We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist. http://www.jcheminf.com/content/2/1/3 Kristina Hettne Antony Williams Erik van Mulligen Jos Kleinjans Valery Tkachenko Jan Kors Journal of Cheminformatics 2010, 2:3 2010-03-23T00:00:00Z doi:10.1186/1758-2946-2-3 Journal of Cheminformatics 1758-2946 2 3 2010-03-23T00:00:00Z XML Background: The virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model. Results: We evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening. Conclusion: The proposed applicability domain formulations for kernel-based QSAR models can successfully identify compounds for which no reliable predictions can be expected from the model. The resulting reduction of the search space and the elimination of some of the active compounds should not be considered as a drawback, because the results indicate that, in most cases, these omitted ligands would not be found by the model anyway. http://www.jcheminf.com/content/2/1/2 Nikolas Fechner Andreas Jahn Georg Hinselmann Andreas Zell Journal of Cheminformatics 2010, 2:2 2010-03-11T00:00:00Z doi:10.1186/1758-2946-2-2 Journal of Cheminformatics 1758-2946 2 2 2010-03-11T00:00:00Z XML A molecule editor, that is program for input and editing of molecules, is an indispensable part of every cheminformatics or molecular processing system. This review focuses on a special type of molecule editors, namely those that are used for molecule structure input on the web. Scientific computing is now moving more and more in the direction of web services and cloud computing, with servers scattered all around the Internet. Thus a web browser has become the universal scientific user interface, and a tool to edit molecules directly within the web browser is essential.The review covers a history of web-based structure input, starting with simple text entry boxes and early molecule editors based on clickable maps, before moving to the current situation dominated by Java applets. One typical example - the popular JME Molecule Editor - will be described in more detail. Modern Ajax server-side molecule editors are also presented. And finally, the possible future direction of web-based molecule editing, based on technologies like JavaScript and Flash, is discussed. http://www.jcheminf.com/content/2/1/1 Peter Ertl Journal of Cheminformatics 2010, 2:1 2010-02-02T00:00:00Z doi:10.1186/1758-2946-2-1 Journal of Cheminformatics 1758-2946 2 1 2010-02-02T00:00:00Z XML Background: The topological maximum cross correlation (TMACC) descriptors are alignment-independent 2D descriptors for the derivation of QSARs. TMACC descriptors are generated using atomic properties determined by molecular topology. Previous validation (J Chem Inf Model 2007, 47: 626-634) of the TMACC descriptor suggests it is competitive with the current state of the art. Results: Here, we illustrate the interpretability of the TMACC descriptors, through the analysis of the QSARs of inhibitors of angiotensin converting enzyme (ACE) and dihydrofolate reductase (DHFR). In the case of the ACE inhibitors, the TMACC interpretation shows features specific to C-domain inhibition, which have not been explicitly identified in previous QSAR studies. Conclusions: The TMACC interpretation can provide new insight into the structure-activity relationships studied. Freely available, open source software for generating the TMACC descriptors can be downloaded from http://comp.chem.nottingham.ac.uk. http://www.jcheminf.com/content/1/1/22 Benson Spowage Craig Bruce Jonathan Hirst Journal of Cheminformatics 2009, 1:22 2009-12-24T00:00:00Z doi:10.1186/1758-2946-1-22 Journal of Cheminformatics 1758-2946 1 22 2009-12-24T00:00:00Z XML Background: There are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets. Results: Pharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of cross-referencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance. Conclusions: Understandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset. http://www.jcheminf.com/content/1/1/21 Amanda Schierz Journal of Cheminformatics 2009, 1:21 2009-12-22T00:00:00Z doi:10.1186/1758-2946-1-21 Journal of Cheminformatics 1758-2946 1 21 2009-12-22T00:00:00Z XML PubChem is an important public, Web-based information source for chemical and bioactivity information. In order to provide convenient structure search methods on compounds stored in this database, one mandatory component is a Web-based drawing tool for interactive sketching of chemical query structures. Web-enabled chemical structure sketchers are not new, being in existence for years; however, solutions available rely on complex technology like Java applets or platform-dependent plug-ins. Due to general policy and support incident rate considerations, Java-based or platform-specific sketchers cannot be deployed as a part of public NCBI Web services. Our solution: a chemical structure sketching tool based exclusively on CGI server processing, client-side JavaScript functions, and image sequence streaming. The PubChem structure editor does not require the presence of any specific runtime support libraries or browser configurations on the client. It is completely platform-independent and verified to work on all major Web browsers, including older ones without support for Web2.0 JavaScript objects. http://www.jcheminf.com/content/1/1/20 Wolf-D. Ihlenfeldt Evan Bolton Stephen Bryant Journal of Cheminformatics 2009, 1:20 2009-12-17T00:00:00Z doi:10.1186/1758-2946-1-20 Journal of Cheminformatics 1758-2946 1 20 2009-12-17T00:00:00Z XML