A universal approach to web-based chemistry using XML and CML

Murray‐Rust, Peter; Rzepa, Henry; Wright, Michael; Zara, Stephen J.

doi:10.1039/b002483j

Cited by 21 publications

(15 citation statements)

References 2 publications

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“…In addition, more flexible input files need to be developed. The extensible markup language is used for input files because of the possibility to annotate the contents and for ease of porting to other applications 17. Some kind of context‐sensitive folding editor is developed (or ported from a different software) to keep the input comprehensible.…”

Section: Introductionmentioning

confidence: 99%

GROMACS—the road ahead

Spoel

Hess

2011

WIREs Comput Mol Sci

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Molecular dynamics (MD) simulations form a powerful tool that is complementary to experiments and theory. They allow detailed investigations of both biological and chemical systems at the atomic level at timescales ranging from femtoseconds to milliseconds. Mechanisms and processes not accessible to experimental techniques can be followed in ‘real time’, and hypotheses based on experiments or theoretical arguments can be tested. Limits on the accuracy of results are mainly due to the physical models, the ratio of the complexity of the problem and the amount of computer time. Here, we review the state of the art in MD simulations with a focus on imminent challenges for the GROMACS (GROningen MAchine for Chemical Simulation) software. New hardware puts new requirements on software, while the breadth of applications and the amount of physical models implemented are increasing rapidly, highlighting shortcomings in the architecture of the programs. We sketch a road map for a popular scientific software package and discuss some of the choices to be made. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 710–715 DOI: 10.1002/wcms.50 This article is categorized under: Software > Molecular Modeling

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Section: Introductionmentioning

confidence: 99%

GROMACS—the road ahead

Spoel

Hess

2011

WIREs Comput Mol Sci

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“…This facilitates the study of a wide range of chemical subdomains which vary in syntactic style, vocabulary and semantic abstraction. Moreover, it is possible to convert ChemicalTagger's output into CML [ 22 ] using a ChemicalTagger2CML converter. Thus, identified phrase-based chemistry such as solutions, reaction and procedures can converted into computable CML.…”

Section: Methodsmentioning

confidence: 99%

ChemicalTagger: A tool for semantic text-mining in chemistry

et al. 2011

Self Cite

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BackgroundThe primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches.ResultsWe have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names).ConclusionsIt is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.

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“…Its conventions guarantee that the data is searchable, stylable, sortable, validatable, transformable, mergeable, transmittable, printable, and even human readable. 21 A strongly simplified example of an hteML-conforming data structure is shown in Fig. 4, and another example is given later in this paper ͑see Fig.…”

Section: Data Structures: Htemlmentioning

confidence: 99%

Using open-source software technologies and standardized data structures to build advanced applications for high-throughput experimentation environments

Zech

Sundermann

Födisch

et al. 2005

Review of Scientific Instruments

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Herein we present a modular approach to a high-throughput experimentation software environment. Instead of a monolithic master system, small tools with a limited set of tasks are interconnected using standardized, self-descriptive data structures. This approach is highly flexible with respect to the rapidly changing needs of the scientists: Since the modules are isolated and intermodule communication is standardized, new components can be integrated without side effects. The developed software environment follows to a large extent the UNIX design philosophy and is heavily based on open-source software technologies that are used to solve specific tasks within the overall system to achieve high productivity in using the software for ambitious high-throughput experimentation programs. It is shown that the orchestration of the system significantly benefits from clear and standardized interface design based on hteML, the high-throughput experimentation markup language, an XML language for the description of high-throughput experimentation data and processes.

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A universal approach to web-based chemistry using XML and CML

Cited by 21 publications

References 2 publications

GROMACS—the road ahead

GROMACS—the road ahead

ChemicalTagger: A tool for semantic text-mining in chemistry

Using open-source software technologies and standardized data structures to build advanced applications for high-throughput experimentation environments

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