Пользовательский интерфейс для извлечения химико-структурной информации из систематического названия органического соединения

The user's interface «Nomenclature Generator» for extraction of the chemical structure information from the systematic name of organic compound represented according to IUPAC nomenclature is developed at the All-Russian Institute for Scientific and Technical Information of Russian Academy of Sciences.В ВИНИТИ РАН разработан пользовательский интерфейс «Номенклатурный Генератор», предназначенный для автоматического извлечения химико-структурной информации из систематического названия органического соединения, данного в номенклатуре ИЮПАК

Пользовательский интерфейс для извлечения химико-структурной информации из систематического названия органического соединения

В ВИНИТИ РАН разработан пользовательский интерфейс «Номенклатурный Генератор», предназначенный для автоматического извлечения химико-структурной информации из систематического названия органического соединения, данного в номенклатуре ИЮПАК

Visualisation, VISC and scientific insight

CISRG discussion paper ;

Foreign Language Translation of Chemical Nomenclature by Computer

Chemical compound names remain the primary method for conveying molecular structures between chemists and researchers. In research articles, patents, chemical catalogues, government legislation, and textbooks, the use of IUPAC and traditional compound names is universal, despite efforts to introduce more machine-friendly representations such as identifiers and line notations. Fortunately, advances in computing power now allow chemical names to be parsed and generated (read and written) with almost the same ease as conventional connection tables. A significant complication, however, is that although the vast majority of chemistry uses English nomenclature, a significant fraction is in other languages. This complicates the task of filing and analyzing chemical patents, purchasing from compound vendors, and text mining research articles or Web pages. We describe some issues with manipulating chemical names in various languages, including British, American, German, Japanese, Chinese, Spanish, Swedish, Polish, and Hungarian, and describe the current state-of-the-art in software tools to simplify the process

Extraction of chemical structures and reactions from the literature

The ever increasing quantity of chemical literature necessitates the creation of automated techniques for extracting relevant information. This work focuses on two aspects: the conversion of chemical names to computer readable structure representations and the extraction of chemical reactions from text. Chemical names are a common way of communicating chemical structure information. OPSIN (Open Parser for Systematic IUPAC Nomenclature), an open source, freely available algorithm for converting chemical names to structures was developed. OPSIN employs a regular grammar to direct tokenisation and parsing leading to the generation of an XML parse tree. Nomenclature operations are applied successively to the tree with many requiring the manipulation of an in-memory connection table representation of the structure under construction. Areas of nomenclature supported are described with attention being drawn to difficulties that may be encountered in name to structure conversion. Results on sets of generated names and names extracted from patents are presented. On generated names, recall of between 96.2% and 99.0% was achieved with a lower bound of 97.9% on precision with all results either being comparable or superior to the tested commercial solutions. On the patent names OPSIN s recall was 2-10% higher than the tested solutions when the patent names were processed as found in the patents. The uses of OPSIN as a web service and as a tool for identifying chemical names in text are shown to demonstrate the direct utility of this algorithm. A software system for extracting chemical reactions from the text of chemical patents was developed. The system relies on the output of ChemicalTagger, a tool for tagging words and identifying phrases of importance in experimental chemistry text. Improvements to this tool required to facilitate this task are documented. The structure of chemical entities are where possible determined using OPSIN in conjunction with a dictionary of name to structure relationships. Extracted reactions are atom mapped to confirm that they are chemically consistent. 424,621 atom mapped reactions were extracted from 65,034 organic chemistry USPTO patents. On a sample of 100 of these extracted reactions chemical entities were identified with 96.4% recall and 88.9% precision. Quantities could be associated with reagents in 98.8% of cases and 64.9% of cases for products whilst the correct role was assigned to chemical entities in 91.8% of cases. Qualitatively the system captured the essence of the reaction in 95% of cases. This system is expected to be useful in the creation of searchable databases of reactions from chemical patents and in facilitating analysis of the properties of large populations of reactions

Methods in literature-based drug discovery

This dissertation work implemented two literature-based methods for predicting new therapeutic uses for drugs, or drug reprofiling (also known as drug repositioning or drug repurposing). Both methods used data stored in ChemoText, a repository of MeSH terms extracted from Medline records and created and designed to support drug discovery algorithms. The first method was an implementation of Swanson's ABC paradigm that used explicit connections between disease, protein, and chemical annotations to find implicit connections between drugs and disease that could be potential new therapeutic drug treatments. The validation approach implemented in the ABC study divided the corpus into two segments based on a year cutoff. The data in the earlier or baseline period was used to create the hypotheses, and the later period data was used to validate the hypotheses. Ranking approaches were used to put the likeliest drug reprofiling candidates near the top of the hypothesis set. The approaches were successful at reproducing Swanson's link between magnesium and migraine and at identifying other significant reprofiled drugs. The second literature-based discovery method used the patterns in side effect annotations to predict drug molecular activity, specifically 5-HT6 binding and dopamine antagonism. Following a study design adopted from QSAR experiments, side effect information for chemicals with known activity was input as binary vectors into classification algorithms. Models were trained on this data to predict the molecular activity. When the best validated models were applied to a large set of chemicals in a virtual screening step, they successfully identified known 5-HT6 binders and dopamine antagonists based solely on side effect profiles. Both studies addressed research areas relevant to current drug discovery, and both studies incorporated rigorous validation steps. For these reasons, the text mining methods presented here, in addition to the ChemoText repository, have the potential to be adopted in the computational drug discovery laboratory and integrated into existing toolsets