http://www.jcheminf.com The latest research articles published by Journal of Cheminformatics 2012-02-02T00:00:00Z 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 PDF 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 PDF Background: The use of SharePoint(R) 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 TM 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 AwareTM 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 AwareTM 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 PDF Background: The computational processing and analysis of small molecules is at heart of cheminformatics and structural bioinformatics and their application in e.g. metabolomics or drug discovery. Pipelining or workflow tools allow for the Lego(TM)-like, graphical assembly of I/O modules and algorithms into a complex workflow which can be easily deployed, modified and tested without the hassle of implementing it into a monolithic application. The CDK-Taverna project aims at building a free open-source cheminformatics pipelining solution through combination of different open-source projects such as Taverna, the Chemistry Development Kit (CDK) or the Waikato Environment for Knowledge Analysis (WEKA). A first integrated version 1.0 of CDK-Taverna was recently released to the public. Results: The CDK-Taverna project was migrated to the most up-to-date versions of its foundational software libraries with a complete re-engineering of its worker's architecture (version 2.0). 64-bit computing and multi-core usage by paralleled threads are now supported to allow for fast in-memory processing and analysis of large sets of molecules. Earlier deficiencies like workarounds for iterative data reading are removed. The combinatorial chemistry related reaction enumeration features are considerably enhanced. Additional functionality for calculating a natural product likeness score for small molecules is implemented to identify possible drug candidates. Finally the data analysis capabilities are extended with new workers that provide access to the open-source WEKA library for clustering and machine learning as well as training and test set partitioning. The new features are outlined with usage scenarios. Conclusions: CDK-Taverna 2.0 as an open-source cheminformatics workflow solution matured to become a freely available and increasingly powerful tool for the biosciences. The combination of the new CDK-Taverna worker family with the already available workflows developed by a lively Taverna community and published on myexperiment.org enables molecular scientists to quickly calculate, process and analyse molecular data as typically found in e.g. today's systems biology scenarios. http://www.jcheminf.com/content/3/1/54 Andreas Truszkowski Kalai Vanii Jayaseelan Stefan Neumann Egon Willighagen Achim Zielesny Christoph Steinbeck Journal of Cheminformatics 2011, null:54 2011-12-13T00:00:00Z doi:10.1186/1758-2946-3-54 New developments on CDK-Taverna The most up to date version of the CDK-Taverna project is described, which aims at building a free open-source cheminformatics pipelining solution through a combination of different open-source projects /content/figures/1758-2946-3-54-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 54 2011-12-13T00:00:00Z PDF MyChemise (My Chemical Structure Editor) is a new 2D structure editor. It is designed as a Java applet that enables the direct creation of structures in the Internet using a web browser. MyChemise saves files in a digital format (.cse) and the import and export of .mol files using the appropriate connection tables is also possible.MyChemise is available as a free online version in English and German. The MyChemise GUI is designed to be user friendly and can be used intuitively. There is also an English and German program description available as a PDF file.In addition to the known ways of drawing chemical structure formulas, there are also parts implemented in the program that allow the creation of different types of presentation. The morphing module uses this technology as a component for dynamic visualisation. For example, it enables a clear and simple illustration of molecule vibrations and reaction sequences. http://www.jcheminf.com/content/3/1/53 Jorg-Hubertus Wilhelm Journal of Cheminformatics 2011, null:53 2011-12-12T00:00:00Z doi:10.1186/1758-2946-3-53 /content/figures/1758-2946-3-53-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 53 2011-12-12T00:00:00Z XML Background: A significant portion of the biomedical and chemical literature refers to small molecules. The accurate identification and annotation of compound name that are relevant to the topic of the given literature can establish links between scientific publications and various chemical and life science databases. Manual annotation is the preferred method for these works because well-trained indexers can understand the paper topics as well as recognize key terms. However, considering the hundreds of thousands of new papers published annually, an automatic annotation system with high precision and relevance can be a useful complement to manual annotation. Results: An automated chemical name annotation system, MeSH Automated Annotations (MAA), was developed to annotate small molecule names in scientific abstracts with tunable accuracy. This system aims to reproduce the MeSH term annotations on biomedical and chemical literature that would be created by indexers. When comparing automated free text matching to those indexed manually of 26 thousand MEDLINE abstracts, more than 40% of the annotations were false-positive (FP) cases. To reduce the FP rate, MAA incorporated several filters to remove "incorrect" annotations caused by nonspecific, partial, and low relevance chemical names. In part, relevance was measured by the position of the chemical name in the text. Tunable accuracy was obtained by adding or restricting the sections of the text scanned for chemical names. The best precision obtained was 96% with a 28% recall rate. The best performance of MAA, as measured with the F statistic was 66%, which favorably compares to other chemical name annotation systems. Conclusions: Accurate chemical name annotation can help researchers not only identify important chemical names in abstracts, but also match unindexed and unstructured abstracts to chemical records. The current work is tested against MEDLINE, but the algorithm is not specific to this corpus and it is possible that the algorithm can be applied to papers from chemical physics, material, polymer and environmental science, as well as patents, biological assay descriptions and other textual data. http://www.jcheminf.com/content/3/1/52 Jun Zhang Lewis Geer Evan Bolton Stephen Bryant Journal of Cheminformatics 2011, null:52 2011-11-22T00:00:00Z doi:10.1186/1758-2946-3-52 Automated annotation of chemical names An automated chemical name annotation system has been developed to annotate small molecule names in scientific abstracts with the aim of reproducing MeSH term annotations on biomedical and chemical literature /content/figures/1758-2946-3-52-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 52 2011-11-22T00:00:00Z PDF Background: In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training. Results: In trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight. Conclusions: Despite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model. http://www.jcheminf.com/content/3/1/51 Patrick McCarren Clayton Springer Lewis Whitehead Journal of Cheminformatics 2011, null:51 2011-11-22T00:00:00Z doi:10.1186/1758-2946-3-51 /content/figures/1758-2946-3-51-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 51 2011-11-22T00:00:00Z PDF Background: Quantitative structure-activity relationships (QSAR) analysis of peptides is helpful for designing various types of drugs such as kinase inhibitor or antigen. Capturing various properties of peptides is essential for analyzing two-dimensional QSAR. A descriptor of peptides is an important element for capturing properties. The atom pair holographic (APH) code is designed for the description of peptides and it represents peptides as the combination of thirty-six types of key atoms and their intermediate binding between two key atoms. Results: The substructure pair descriptor (SPAD) represents peptides as the combination of forty-nine types of key substructures and the sequence of amino acid residues between two substructures. The size of the key substructures is larger and the length of the sequence is longer than traditional descriptors. Similarity searches on C5a inhibitor data set and kinase inhibitor data set showed that order of inhibitors become three times higher by representing peptides with SPAD, respectively. Comparing scope of each descriptor shows that SPAD captures different properties from APH. Conclusion: QSAR/QSPR for peptides is helpful for designing various types of drugs such as kinase inhibitor and antigen. SPAD is a novel and powerful descriptor for various types of peptides. Accuracy of QSAR/QSPR becomes higher by describing peptides with SPAD. http://www.jcheminf.com/content/3/1/50 Tsutomu Osoda Satoru Miyano Journal of Cheminformatics 2011, null:50 2011-11-02T00:00:00Z doi:10.1186/1758-2946-3-50 /content/figures/1758-2946-3-50-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 50 2011-11-02T00:00:00Z XML Background: The Elk-1 transcription factor is a member of a group of proteins called ternary complex factors, which serve as a paradigm for gene regulation in response to extracellular signals. Its deregulation has been linked to multiple human diseases including the development of tumours. The work herein aims to inform the design of potential peptidomimetic compounds that can inhibit the formation of the Elk-1 dimer, which is key to Elk-1 stability. We have conducted molecular dynamics simulations of the Elk-1 ETS domain followed by virtual screening. Results: We show the ETS dimerisation site undergoes conformational reorganisation at the α1β1 loop. Through exhaustive screening of di- and tri-peptide libraries against a collection of ETS domain conformations representing the dynamics of the loop, we identified a series of potential binders for the Elk-1 dimer interface. The di-peptides showed no particular preference toward the binding site; however, the tri-peptides made specific interactions with residues: Glu17, Gln18 and Arg49 that are pivotal to the dimer interface. Conclusions: We have shown molecular dynamics simulations can be combined with virtual peptide screening to obtain an exhaustive docking protocol that incorporates dynamic fluctuations in a receptor. Based on our findings, we suggest experimental binding studies to be performed on the 12 SILE ranked tri-peptides as possible compounds for the design of inhibitors of Elk-1 dimerisation. It would also be reasonable to consider the score-ranked tri-peptides as a comparative test to establish whether peptide size is a determinant factor of binding to the ETS domain. http://www.jcheminf.com/content/3/1/49 Abrar Hussain Peter Shaw Jonathan Hirst Journal of Cheminformatics 2011, null:49 2011-11-01T00:00:00Z doi:10.1186/1758-2946-3-49 /content/figures/1758-2946-3-49-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 49 2011-11-01T00:00:00Z XML CMLLite is a collection of definitions and processes which provide strong and flexible validation for a document in Chemical Markup Language (CML). It consists of an updated CML schema (schema3), conventions specifying rules in both human and machine-understandable forms and a validator available both online and offline to check conformance. This article explores the rationale behind the changes which have been made to the schema, explains how conventions interact and how they are designed, formulated, implemented and tested, and gives an overview of the validation service. http://www.jcheminf.com/content/3/1/39 Joe Townsend Peter Murray-Rust Journal of Cheminformatics 2011, null:39 2011-10-14T00:00:00Z doi:10.1186/1758-2946-3-39 /content/figures/1758-2946-3-39-toc.gif Journal of Cheminformatics 1758-2946 ${item.volume} 39 2011-10-14T00:00:00Z XML