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Chemical datuments as scientific enablers.

ABSTRACT: This article is an attempt to construct a chemical datument as a means of presenting insights into chemical phenomena in a scientific journal. An exploration of the interactions present in a small fragment of duplex Z-DNA and the nature of the catalytic centre of a carbon-dioxide/alkene epoxide alternating co-polymerisation is presented in this datument, with examples of the use of three software tools, one based on Java, the other two using Javascript and HTML5 technologies. The implications for the evolution of scientific journals are discussed.

SUBMITTER: Rzepa HS

PROVIDER: S-EPMC3552767 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Chemical datuments as scientific enablers.

Rzepa Henry S HS

Journal of cheminformatics 20130123 1

This article is an attempt to construct a chemical datument as a means of presenting insights into chemical phenomena in a scientific journal. An exploration of the interactions present in a small fragment of duplex Z-DNA and the nature of the catalytic centre of a carbon-dioxide/alkene epoxide alternating co-polymerisation is presented in this datument, with examples of the use of three software tools, one based on Java, the other two using Javascript and HTML5 technologies. The implications fo ...[more]

PMID: 23343381

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Project description:MotivationApplication of chemical named entity recognition (CNER) algorithms allows retrieval of information from texts about chemical compound identifiers and creates associations with physical-chemical properties and biological activities. Scientific texts represent low-formalized sources of information. Most methods aimed at CNER are based on machine learning approaches, including conditional random fields and deep neural networks. In general, most machine learning approaches require either vector or sparse word representation of texts. Chemical named entities (CNEs) constitute only a small fraction of the whole text, and the datasets used for training are highly imbalanced.Methods and resultsWe propose a new method for extracting CNEs from texts based on the naïve Bayes classifier combined with specially developed filters. In contrast to the earlier developed CNER methods, our approach uses the representation of the data as a set of fragments of text (FoTs) with the subsequent preparati`on of a set of multi-n-grams (sequences from one to n symbols) for each FoT. Our approach may provide the recognition of novel CNEs. For CHEMDNER corpus, the values of the sensitivity (recall) was 0.95, precision was 0.74, specificity was 0.88, and balanced accuracy was 0.92 based on five-fold cross validation. We applied the developed algorithm to the extracted CNEs of potential Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) inhibitors. A set of CNEs corresponding to the chemical substances evaluated in the biochemical assays used for the discovery of Mpro inhibitors was retrieved. Manual analysis of the appropriate texts showed that CNEs of potential SARS-CoV-2 Mpro inhibitors were successfully identified by our method.ConclusionThe obtained results show that the proposed method can be used for filtering out words that are not related to CNEs; therefore, it can be successfully applied to the extraction of CNEs for the purposes of cheminformatics and medicinal chemistry.

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Chemical datuments as scientific enablers.

Publications

Chemical datuments as scientific enablers.

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