http://www.jcheminf.com The latest research articles published by Journal of Cheminformatics 2012-05-15T00:00:00Z Background: Experimental screening of chemical compounds for biological activity is a time consuming and expensive practice. In silico predictive models permit inexpensive, rapid "virtual screening" to prioritize selection of compounds for experimental testing. Both experimental and in silico screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of in silico predictive classification models for prediction of mutagenetic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics. Results: The Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data seta in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naive Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used the approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model. Conclusion: We have created a new mutagenicity benchmark data set with around 8,000 compounds. Our work shows that highly accurate predictive mutagenicity models can be built using machine learning methods based on chemical descriptors and trained using this set, and these models provide a complement to toxicophores based methods. Further, our work supports other recent literature in showing that Random Forest models generally outperform other comparable machine learning methods for this kind of application. http://www.jcheminf.com/content/4/1/10 Abhik Seal Anurag Passi UC Abdul Jaleel David Wild OSDD Consortium Journal of Cheminformatics 2012, null:10 2012-05-15T00:00:00Z doi:10.1186/1758-2946-4-10 /content/figures/1758-2946-4-10-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 10 2012-05-15T00:00:00Z PDF Background: Terms representing chemical concepts found the Unified Medical Language System (UMLS) are used to derive an expanded semantic network with mutually exclusive semantic types. The UMLS Semantic Network (SN) is composed of a collection of broad categories calledsemantic types (STs) that are assigned to concepts. Within the UMLS's coverage of the chemical domain, we find a great deal of concepts being assigned more than one ST. This leads to the situation where the extent of a given ST may contain concepts elaborating variegated semantics. A methodology for expanding the chemical subhierarchy of the SN into a finer-grained categorization of mutually exclusive types with semantically uniform extents is presented. We call this network a Chemical Specialty Semantic Network (CSSN). A CSSN is derived automatically from the existing chemical STs and their assignments. The methodology incorporates a threshold value governing the minimum size of a type's extent needed for inclusion in the CSSN. Thus, different CSSNs can be created by choosing different threshold values based on varying requirements. Results: A complete CSSN is derived using a threshold value of 300 and having 68 STs. It is used effectively to provide high-level categorizations for a random sample of compounds from the "Chemical Entities of Biological Interest" (ChEBI) ontology. The effect on the size of the CSSN using various threshold parameter values between one and 500 is shown. Conclusions: The methodology has several potential applications, including its use to derive a precoordinated guide for ST assignments to new UMLS chemical concepts, as a tool for auditing existing concepts, inter-terminology mapping, and to serve as an upper-level network for ChEBI. http://www.jcheminf.com/content/4/1/9 C. Paul Morrey Yehoshua Perl Michael Halper Ling Chen Huanying Gu Journal of Cheminformatics 2012, null:9 2012-05-11T00:00:00Z doi:10.1186/1758-2946-4-9 /content/figures/1758-2946-4-9-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 9 2012-05-11T00:00:00Z PDF Background: Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies. Results: We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches. Conclusion: Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research. http://www.jcheminf.com/content/4/1/8 Janna Hastings Despoina Magka Colin Batchelor Lian Duan Robert Stevens Marcus Ennis Christoph Steinbeck Journal of Cheminformatics 2012, null:8 2012-04-05T00:00:00Z doi:10.1186/1758-2946-4-8 /content/figures/1758-2946-4-8-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 8 2012-04-05T00:00:00Z PDF Analyzing chemical datasets is a challenging task for scientific researchers in the field of chemoinformatics. It is important, yet difficult to understand the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. To that respect, visualization tools can help to better comprehend the underlying correlations. Our recently developed 3D molecular viewer CheS-Mapper (Chemical Space Mapper) divides large datasets into clusters of similar compounds and consequently arranges them in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kind of features, like structural fragments as well as quantitative chemical descriptors. These features can be highlighted within CheS-Mapper, which aids the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. As a final function, the tool can also be used to select and export specific subsets of a given dataset for further analysis. http://www.jcheminf.com/content/4/1/7 Martin Gutlein Andreas Karwath Stefan Kramer Journal of Cheminformatics 2012, null:7 2012-03-17T00:00:00Z doi:10.1186/1758-2946-4-7 Chemical Space Mapping and Visualization in 3D Analyzing chemical datasets is an important task for understanding structure-property relationships. Visualization tools can help to better comprehend the correlations, and in this paper, an open-source tool for inspecting small molecule datasets is presented /content/figures/1758-2946-4-7-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 7 2012-03-17T00:00:00Z XML Background: Systems chemical biology and chemogenomics are considered critical, integrative disciplines in modern biomedical research, but require data mining of large, integrated, heterogeneous datasets from chemistry and biology. We previously developed an RDF-based resource called Chem2Bio2RDF that enabled querying of such data using the SPARQL query language. Whilst this work has proved useful in its own right as one of the first major resources in these disciplines, its utility could be greatly improved by the application of an ontology for annotation of the nodes and edges in the RDF graph, enabling a much richer range of semantic queries to be issued. Results: We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology.AvailabilityChem2Bio2OWL is available at http://chem2bio2rdf.org/owl. The document is available at http://chem2bio2owl.wikispaces.com. http://www.jcheminf.com/content/4/1/6 Bin Chen Ying Ding David Wild Journal of Cheminformatics 2012, null:6 2012-03-08T00:00:00Z doi:10.1186/1758-2946-4-6 /content/figures/1758-2946-4-6-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 6 2012-03-08T00:00:00Z XML Background: One of the largest challenges in chemistry today remains that of efficiently mining through vast amounts of data in order to elucidate the chemical structure for an unknown compound. The elucidated candidate compound must be fully consistent with the data and any other competing candidates efficiently eliminated without doubt by using additional data if necessary. It has become increasingly necessary to incorporate an in silico structure generation and verification tool to facilitate this elucidation process. An effective structure elucidation software technology aims to mimic the skills of a human in interpreting the complex nature of spectral data while producing a solution within a reasonable amount of time. This type of software is known as computer-assisted structure elucidation or CASE software. A systematic trial of the ACD/Structure Elucidator CASE software was conducted over an extended period of time by analysing a set of single and double-blind trials submitted by a global audience of scientists. The purpose of the blind trials was to reduce subjective bias. Double-blind trials comprised of data where the candidate compound was unknown to both the submitting scientist and the analyst. The level of expertise of the submitting scientist ranged from novice to expert structure elucidation specialists with experience in pharmaceutical, industrial, government and academic environments. Results: Beginning in 2003, and for the following nine years, the algorithms and software technology contained within ACD/Structure Elucidator have been tested against 112 data sets; many of these were unique challenges. Of these challenges 9% were double-blind trials. The results of eighteen of the single-blind trials were investigated in detail and included problems of a diverse nature with many of the specific challenges associated with algorithmic structure elucidation such as deficiency in protons, structure symmetry, a large number of heteroatoms and poor quality spectral data. Conclusion: When applied to a complex set of blind trials, ACD/Structure Elucidator was shown to be a very useful tool in advancing the computer's contribution to elucidating a candidate structure from a set of spectral data (NMR and MS) for an unknown. The synergistic interaction between humans and computers can be highly beneficial in terms of less biased approaches to elucidation as well as dramatic improvements in speed and throughput. In those cases where multiple candidate structures exist, ACD/Structure Elucidator is equipped to validate the correct structure and eliminate inconsistent candidates. Full elucidation can generally be performed in less than two hours; this includes the average spectral data processing time and data input. http://www.jcheminf.com/content/4/1/5 Arvin Moser Mikhail Elyashberg Antony Williams Kirill Blinov Joseph DiMartino Journal of Cheminformatics 2012, null:5 2012-02-09T00:00:00Z doi:10.1186/1758-2946-4-5 /content/figures/1758-2946-4-5-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 5 2012-02-09T00:00:00Z XML Background: The current chemical space of known small molecules is estimated to exceed 1060 structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors. Results: The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree. Conclusions: Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings. http://www.jcheminf.com/content/4/1/4 Narender Singh Hongmao Sun Sidhartha Chaudhury Mohamed AbdulHameed Anders Wallqvist Gregory Tawa Journal of Cheminformatics 2012, null:4 2012-02-08T00:00:00Z doi:10.1186/1758-2946-4-4 /content/figures/1758-2946-4-4-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 4 2012-02-08T00:00:00Z XML Background: Representations of chemical datasets in spreadsheet format are important for ready data assimilation and manipulation. In addition to the normal spreadsheet facilities, chemical spreadsheets need to have visualisable chemical structures and data searchable by chemical as well as textual queries. Many such chemical spreadsheet tools are available, some operating in the familiar Microsoft Excel environment. However, within this group, the performance of Excel is often compromised, particularly in terms of the number of compounds which can usefully be stored on a sheet.SummaryLICSS is a lightweight chemical spreadsheet within Microsoft Excel for Windows. LICSS stores structures solely as Smiles strings. Chemical operations are carried out by calling Java code modules which use the CDK, JChemPaint and OPSIN libraries to provide cheminformatics functionality. Compounds in sheets or charts may be visualised (individually or en masse), and sheets may be searched by substructure or similarity. All the molecular descriptors available in CDK may be calculated for compounds (in batch or on-the-fly), and various cheminformatic operations such as fingerprint calculation, Sammon mapping, clustering and R group table creation may be carried out.We detail here the features of LICSS and how they are implemented. We also explain the design criteria, particularly in terms of potential corporate use, which led to this particular implementation. Conclusions: LICSS is an Excel-based chemical spreadsheet with a difference:• It can usefully be used on sheets containing hundreds of thousands of compounds; it doesn't compromise the normal performance of Microsoft Excel• It is designed to be installed and run in environments in which users do not have admin privileges; installation involves merely file copying, and sharing of LICSS sheets invokes automatic installation• It is free and extensibleLICSS is open source software and we hope sufficient detail is provided here to enable developers to add their own features and share with the community. http://www.jcheminf.com/content/4/1/3 Kevin Lawson Jonty Lawson Journal of Cheminformatics 2012, null:3 2012-02-02T00:00:00Z doi:10.1186/1758-2946-4-3 A chemical spreadsheet in Excel A lightweight open source chemical spreadsheet has been developed that runs within Microsoft Excel and can be used on sheets containing hundreds of thousands of compounds without compromising normal performance /content/figures/1758-2946-4-3-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 3 2012-02-02T00:00:00Z XML The mechanism of phospholipidosis is still not well understood. Numerous different mechanisms have been proposed, varying from direct inhibition of the breakdown of phospholipids to the binding of a drug compound to the phospholipid, preventing breakdown. We have used a probabilistic method, the Parzen-Rosenblatt Window approach, to build a model from the ChEMBL dataset which can predict from a compound's structure both its primary pharmaceutical target and other targets with which it forms off-target, usually weaker, interactions. Using a small dataset of 182 phospholipidosis-inducing and non-inducing compounds, we predict their off-target activity against targets which could relate to phospholipidosis as a side-effect of a drug. We link these targets to specific mechanisms of inducing this lysosomal build-up of phospholipids in cells. Thus, we show that the induction of phospholipidosis is likely to occur by separate mechanisms when triggered by different cationic amphiphilic drugs. We find that both inhibition of phospholipase activity and enhanced cholesterol biosynthesis are likely to be important mechanisms. Furthermore, we provide evidence suggesting four specific protein targets. Sphingomyelin phosphodiesterase, phospholipase A2 and lysosomal phospholipase A1 are shown to be likely targets for the induction of phospholipidosis by inhibition of phospholipase activity, while lanosterol synthase is predicted to be associated with phospholipidosis being induced by enhanced cholesterol biosynthesis. This analysis provides the impetus for further experimental tests of these hypotheses. http://www.jcheminf.com/content/4/1/2 Robert Lowe Hamse Mussa Florian Nigsch Robert Glen John Mitchell Journal of Cheminformatics 2012, null:2 2012-01-26T00:00:00Z doi:10.1186/1758-2946-4-2 Predicting the mechanism of phospholipidosis An in silico approach was used to predict targets for phospholipidosis, a lysosomal disorder characterized by accumulation of phospholipids in tissues. By predicting targets for a database of compounds, they can be ranked by their potential to cause phospholipidosis /content/figures/1758-2946-4-2-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 2 2012-01-26T00:00:00Z XML Background: The use of SharePoint® collaboration software for content management has become a critical part of today's drug discovery process. SharePoint 2010 software has laid a foundation which enables researchers to collaborate and search on various contents. The amount of data generated during a transition of a single compound from preclinical discovery to commercialization can easily range in terabytes, thus there is a greater demand of a chemically aware search algorithm that supplements SharePoint which enables researchers to query for information in a more intuitive and effective way. Thus by supplementing SharePoint with Chemically Aware™ features provides a great value to the pharmaceutical and biotech companies and makes drug discovery more efficient. Using several tools we have integrated SharePoint with chemical, compound, and reaction databases, thereby improving the traditional search engine capability and enhancing the user experience. Results: This paper describes the implementation of a Chemically Aware™ system to supplement SharePoint. A Chemically Aware SharePoint (CASP) allows users to tag documents by drawing a structure and associating it with the related content. It also allows the user to search SharePoint software content and internal/external databases by carrying out substructure, similarity, SMILES, and IUPAC name searches. Building on traditional search, CASP takes SharePoint one step further by providing a intuitive GUI to the researchers to base their search on their knowledge of chemistry than textual search. CASP also provides a way to integrate with other systems, for example a researcher can perform a sub-structure search on pdf documents with embedded molecular entities. Conclusion: A Chemically Aware™ system supplementing SharePoint is a step towards making drug discovery process more efficient and also helps researchers to search for information in a more intuitive way. It also helps the researchers to find information which was once difficult to find by allowing one to tag documents with molecular entities and integrating with image recognition software to find information from pdf documents. http://www.jcheminf.com/content/4/1/1 Kartik Tallapragada Joseph Chewning David Kombo Beverly Ludwick Journal of Cheminformatics 2012, null:1 2012-01-12T00:00:00Z doi:10.1186/1758-2946-4-1 /content/figures/1758-2946-4-1-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 1 2012-01-12T00:00:00Z XML