Search

Abstract This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically-resolved chemical entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically-resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry. Scientific contribution This new extension to the InChI standard would enable improved annotation of analytical datasets characterizing chemical entities, supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles of data stewardship for chemical datasets, ultimately promoting Open Science in chemistry.

This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically-resolved chemical entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically-resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry. Scientific contribution: This new extension to the InChI standard would enable improved annotation of analytical datasets characterizing chemical entities, supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles of data stewardship for chemical datasets, ultimately promoting Open Science in chemistry. [ABSTRACT FROM AUTHOR]

Abstract The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional “QSAR-ready” forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the “QSAR-ready” workflow to generate “MS-ready structures” to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry. [ABSTRACT FROM AUTHOR]

Abstract Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, the Simplified Molecular-Input Line-Entry System and the IUPAC International Chemical Identifier), each offering advantages specific to various use-cases. Representation of the multi-component structures of nanomaterials (NMs), though, remains out of scope for all the currently available standards, as the nature of NMs sets new challenges on formalizing the encoding of their structure, interactions and environmental parameters. In this work we identify a set of principles that a NM representation should adhere to in order to provide “machine-friendly” encodings of NMs, i.e. encodings that facilitate machine processing and cooperation with nanoinformatics tools. We illustrate our principles by showing how the recently introduced InChI-based NM representation, might be augmented, in principle, to also encode morphology and mixture properties, distributions of properties, and also to capture auxiliary information and allow data reuse.

Abstract Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development.

Efficient and machine-readable representations are needed to accurately identify, validate and communicate information of chemical structures. Many such representations have been developed (as, for example, the Simplified Molecular-Input Line-Entry System and the IUPAC International Chemical Identifier), each offering advantages specific to various use-cases. Representation of the multi-component structures of nanomaterials (NMs), though, remains out of scope for all the currently available standards, as the nature of NMs sets new challenges on formalizing the encoding of their structure, interactions and environmental parameters. In this work we identify a set of principles that a NM representation should adhere to in order to provide "machine-friendly" encodings of NMs, i.e. encodings that facilitate machine processing and cooperation with nanoinformatics tools. We illustrate our principles by showing how the recently introduced InChI-based NM representation, might be augmented, in principle, to also encode morphology and mixture properties, distributions of properties, and also to capture auxiliary information and allow data reuse. [ABSTRACT FROM AUTHOR]

This article discusses the ways of linking physical objects to digital information relevant to chemical entities, specifically those that can be described by the use of the IUPAC International Chemical Identifier (InChI). It makes recommendations on the form of the computer readable components of labels provided for chemicals and materials that are used on product/sample containers and on the associated documentation that is used when transporting these containers (either internally or during export/import). The focus is on specification of the content of the 2D Quick Response bar codes required to describe the molecular content of the containers and link to digital resources to supplement that provided on a physical label. The necessary technical and (possible) business infrastructure necessary to support the use of the InChI and InChIKey for rapid recall of relevant information is considered here and suggestions are made. [ABSTRACT FROM AUTHOR]

Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development. [ABSTRACT FROM AUTHOR]

Abstract We present a sequence-to-sequence machine learning model for predicting the IUPAC name of a chemical from its standard International Chemical Identifier (InChI). The model uses two stacks of transformers in an encoder-decoder architecture, a setup similar to the neural networks used in state-of-the-art machine translation. Unlike neural machine translation, which usually tokenizes input and output into words or sub-words, our model processes the InChI and predicts the IUPAC name character by character. The model was trained on a dataset of 10 million InChI/IUPAC name pairs freely downloaded from the National Library of Medicine’s online PubChem service. Training took seven days on a Tesla K80 GPU, and the model achieved a test set accuracy of 91%. The model performed particularly well on organics, with the exception of macrocycles, and was comparable to commercial IUPAC name generation software. The predictions were less accurate for inorganic and organometallic compounds. This can be explained by inherent limitations of standard InChI for representing inorganics, as well as low coverage in the training data.

Horizontal gene transfer (HGT) by plasmid conjugation is a major driving force in the spread of antibiotic resistance among Enterobacteriaceae. Most of the conjugation studies are based on calculation of conjugation ratios (number of transconjugants/number of donors) after viable counting of transconjugant and donor cells. The development of robust, fast and reliable techniques for in situ monitoring and quantification of conjugation ratios might accelerate progress in understanding the impact of this cellular process in the HGT. The IncHI1 plasmids, involved in multiresistance phenotypes of relevant pathogens such as Salmonella and E. coli, are distinguished by the thermosensitivity of their conjugative transfer. Conjugation mediated by IncHI1 plasmids is more efficient at temperatures lower than 30 °C, suggesting that the transfer process takes place during the environmental transit of the bacteria. In this report, we described a methodology to monitor in situ the conjugation process during agar surface matings of the IncHI1 plasmid R27 and its derepressed derivative drR27 at different temperatures. A three-color-labeling strategy was used to visualize the spatial distribution of transconjugants within the heterogeneous environment by epifluorescence and confocal microscopy. Moreover, the fluorescent labelling was also used to quantify conjugation frequencies in liquid media by flow cytometry. [ABSTRACT FROM AUTHOR]

To test the RInChI algorithm, the RInChI and the RInChI keys (Long-RInChIKey, Short-RInChIKey and Web-RInChIKey) for the entire reaction part of the database were calculated. Keywords: Cheminformatics; InChI; integrating reaction data; reaction databases; RInChI; SAVI; SPRESI EN Cheminformatics InChI integrating reaction data reaction databases RInChI SAVI SPRESI 643 655 13 08/09/22 20220601 NES 220601 Introduction This is an exciting time in the computational analysis of reactions. Reaction duplicate checks in SPRESI by InfoChem InfoChem GmbH [[18]], Munich, Germany, publishes the chemical database SPRESI containing 5.8 million molecules abstracted from literature, nearly 6.6 million reactions, 700 000 references and 164 000 patents with the major focus on organic chemistry covering the years 1974-2014 [[5]]. Cheminformatics, SAVI, SPRESI, InChI, integrating reaction data, reaction databases, RInChI. [Extracted from the article]

Abstract The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and integration of large chemical databases. InChI version 1.05 was released in January 2017 and version 1.06 in December 2020. In this paper, we report on the current state of the InChI Software, the details of the improvements in the v1.06 release, and the results of a test of the InChI run on PubChem, a database of more than a hundred million molecules. The upgrade introduces significant new features, including support for pseudo-element atoms and an improved description of polymers. We expect that few, if any, applications using the standard InChI will need to change as a result of the changes in version 1.06. Numerical instability was discovered for 0.002% of this database, and a small number of other molecules were discovered for which the algorithm did not run smoothly. On the basis of PubChem data, we can demonstrate that InChI version 1.05 was 99.996% accurate, and InChI version 1.06 represents a step closer to perfection. Finally, we look forward to future releases and extensions for the InChI Chemical identifier.

We present a sequence-to-sequence machine learning model for predicting the IUPAC name of a chemical from its standard International Chemical Identifier (InChI). The model uses two stacks of transformers in an encoder-decoder architecture, a setup similar to the neural networks used in state-of-the-art machine translation. Unlike neural machine translation, which usually tokenizes input and output into words or sub-words, our model processes the InChI and predicts the IUPAC name character by character. The model was trained on a dataset of 10 million InChI/IUPAC name pairs freely downloaded from the National Library of Medicine's online PubChem service. Training took seven days on a Tesla K80 GPU, and the model achieved a test set accuracy of 91%. The model performed particularly well on organics, with the exception of macrocycles, and was comparable to commercial IUPAC name generation software. The predictions were less accurate for inorganic and organometallic compounds. This can be explained by inherent limitations of standard InChI for representing inorganics, as well as low coverage in the training data. [ABSTRACT FROM AUTHOR]

Nontyphoidal Salmonella (NTS) is the most reported cause of bacterial foodborne zoonoses in Vietnam, and contaminated pork is one of the main sources of human infection. In recent years, the prevalence of NTS carrying multiple antimicrobial resistance genes (ARGs) have been increased. The genomic characterization along the pig value chain and the identification of ARGs and plasmids have the potential to improve food safety by understanding the dissemination of ARGs from the farm to the table. We report an analysis of 13 S. Derby and 10 S. Rissen isolates, collected in 2013 at different stages in Vietnamese slaughterhouses and markets. VITEK 2 Compact System was used to characterize the phenotypical antimicrobial resistance of the isolates. In addition, whole-genome sequencing (WGS) was used to detect ARGs and plasmids conferring multidrug resistance. Whole genome single nucleotide polymorphism typing was used to determine the genetic diversity of the strains and the spread of ARGs along the pig value chain. Altogether, 86.9% (20/23) of the samples were resistant to at least one antibiotic. Resistance to ampicillin was most frequently detected (73.9%), followed by piperacillin and moxifloxacin (both 69.6%). At least one ARG was found in all strains, and 69.6% (16/23) were multidrug-resistant (MDR). The observed phenotype and genotype of antimicrobial resistance were not always concordant. Plasmid replicons were found in almost all strains [95.6% (22/23)], and the phylogenetic analysis detected nine clusters (S. Derby, n = 5; S. Rissen, n = 4). ARGs and plasmid content were almost identical within clusters. We found six MDR IncHI1s with identical plasmid sequence type in strains of different genetic clusters at the slaughterhouse and the market. In conclusion, high rates of multidrug resistance were observed in Salmonella strains from Vietnam in 2013. Genomic analysis revealed many resistance genes and plasmids, which have the potential to spread along the pig value chain from the slaughterhouse to the market. This study pointed out that bioinformatics analyses of WGS data are essential to detect, trace back, and control the MDR strains along the pig value chain. Further studies are necessary to assess the more recent MDR Salmonella strains spreading in Vietnam.

Abstract We describe a file format that is designed to represent mixtures of compounds in a way that is fully machine readable. This Mixfile format is intended to fill the same role for substances that are composed of multiple components as the venerable Molfile does for specifying individual structures. This much needed datastructure is intended to replace current practices for communicating information about mixtures, which usually relies on human-readable text descriptions, drawing several species within a single molecular diagram, or mutually incompatible ad hoc solutions. We describe an open source software application for editing mixture files, which can also be used as web-ready tools for manipulating the file format. We also present a corpus of mixture examples, which we have extracted from collections of text-based descriptions. Furthermore, we present an early look at the proposed IUPAC Mixtures InChI specification, instances of which can be automatically generated using the Mixfile format as a precursor.

Horizontal gene transfer (HGT) by plasmid conjugation is a major driving force in the spread of antibiotic resistance among Enterobacteriaceae. Most of the conjugation studies are based on calculation of conjugation ratios (number of transconjugants/number of donors) after viable counting of transconjugant and donor cells. The development of robust, fast and reliable techniques for in situ monitoring and quantification of conjugation ratios might accelerate progress in understanding the impact of this cellular process in the HGT. The IncHI1 plasmids, involved in multiresistance phenotypes of relevant pathogens such as Salmonella and E. coli, are distinguished by the thermosensitivity of their conjugative transfer. Conjugation mediated by IncHI1 plasmids is more efficient at temperatures lower than 30 °C, suggesting that the transfer process takes place during the environmental transit of the bacteria. In this report, we described a methodology to monitor in situ the conjugation process during agar surface matings of the IncHI1 plasmid R27 and its derepressed derivative drR27 at different temperatures. A three-color-labeling strategy was used to visualize the spatial distribution of transconjugants within the heterogeneous environment by epifluorescence and confocal microscopy. Moreover, the fluorescent labelling was also used to quantify conjugation frequencies in liquid media by flow cytometry.

The software for the IUPAC Chemical Identifier, InChI, is extraordinarily reliable. It has been tested on large databases around the world, and has proved itself to be an essential tool in the handling and integration of large chemical databases. InChI version 1.05 was released in January 2017 and version 1.06 in December 2020. In this paper, we report on the current state of the InChI Software, the details of the improvements in the v1.06 release, and the results of a test of the InChI run on PubChem, a database of more than a hundred million molecules. The upgrade introduces significant new features, including support for pseudo-element atoms and an improved description of polymers. We expect that few, if any, applications using the standard InChI will need to change as a result of the changes in version 1.06. Numerical instability was discovered for 0.002% of this database, and a small number of other molecules were discovered for which the algorithm did not run smoothly. On the basis of PubChem data, we can demonstrate that InChI version 1.05 was 99.996% accurate, and InChI version 1.06 represents a step closer to perfection. Finally, we look forward to future releases and extensions for the InChI Chemical identifier. [ABSTRACT FROM AUTHOR]

Abstract Background PubChem is a chemical information repository, consisting of three primary databases: Substance, Compound, and BioAssay. When individual data contributors submit chemical substance descriptions to Substance, the unique chemical structures are extracted and stored into Compound through an automated process called structure standardization. The present study describes the PubChem standardization approaches and analyzes them for their success rates, reasons that cause structures to be rejected, and modifications applied to structures during the standardization process. Furthermore, the PubChem standardization is compared to the structure normalization of the IUPAC International Chemical Identifier (InChI) software, as manifested by conversion of the InChI back into a chemical structure. Results The observed rejection rate for substances processed by PubChem standardization was 0.36%, which is predominantly attributed to structures with invalid atom valences that cannot be readily corrected without additional information from contributors. Of all structures that pass standardization, 44% are modified in the process, reducing the count of unique structures from 53,574,724 in substance to 45,808,881 in compound as identified by de-aromatized canonical isomeric SMILES. Even though the processing time is very low on average (only 0.4% of structures have individual standardization time above 0.1 s), total standardization time is completely dominated by edge cases: 90% of the time to standardize all structures in PubChem substance is spent on the 2.05% of structures with the highest individual standardization time. It is worth noting that 60% of the structures obtained from PubChem structure standardization are not identical to the chemical structure resulting from the InChI (primarily due to preferences for a different tautomeric form). Conclusions Standardization of chemical structures is complicated by the diversity of chemical information and their representations approaches. The PubChem standardization is an effective and efficient tool to account for molecular diversity and to eliminate invalid/incomplete structures. Further development will concentrate on improved tautomer consideration and an expanded stereocenter definition. Modifications are difficult to thoroughly validate, with slight changes often affecting many thousands of structures and various edge cases. The PubChem structure standardization service is accessible as a public resource (https://pubchem.ncbi.nlm.nih.gov/standardize), and via programmatic interfaces.

As the number of natural and synthesized compounds quickly increases, the management of compounds in large databases is becoming a challenging work. Especially for a compound that can be represented by several different graphs such as benzene can be drawn in single and double bonds, or aromatic bonds. These different graphs are the same for the chemists and are called equivalent chemical graphs or equivalent chemical structures herein (H atoms are found at more than one locations are not called equivalent chemical structures such as enol form and keto form). Researchers mostly hope that each group of equivalent chemical structures (represented by different graphs) could be denoted by the same value, and make them easy to be deal with. For this goal a highly discriminating index ATID (adjacent topology identification), which was derived from a graph theoretical index – 3-EAID, was suggested. In order to reduce the chance that the different compounds were mistaken for duplicate compounds, the uniqueness test of ATID was performed by over 60 million alkanes and over 19 million benzenoids with high similarity, and the results indicate that ATID possesses high discriminating ability. Finally, the ATID was successfully applied to retrieval of duplicate compounds in large databases. The results indicate that the ATID could be a valuable tool for chemical information administration. The ATID can give comparable results with InChI (International Chemical Identifier) dependent on atom number and derived from complicated rules. [ABSTRACT FROM AUTHOR]

We are developing a database named 3DMET, a three-dimensional structure database of natural metabolites. There are two major impediments to the creation of 3D chemical structures from a set of planar structure drawings: the limited accuracy of computer programs and insufficient human resources for manual curation. We have tested some 2D-3D converters to convert 2D structure files from external databases. These automatic conversion processes yielded an excessive number of improper conversions. To ascertain the quality of the conversions, we compared InChI and canonical SMILES notations before and after conversion. Structures whose notations correspond to each other were regarded as a correct conversion in our present work. We found that chiral inversion is the most serious factor during the improper conversion. In the current stage of our database construction, published books or articles have been resources for additions to our database. Chemicals are usually drawn as pictures on the paper. To save human resources, an optical structure reader was introduced. The program was quite useful but some particular errors were observed during our operation. We hope our trials for producing correct 3D structures will help other developers of chemical programs and curators of chemical databases.

This paper documents the design, layout and algorithms of the IUPAC International Chemical Identifier, InChI. [ABSTRACT FROM AUTHOR]

The yaChI is a newly suggested chemical naming system. However, yaChI is a derivative of the IUPAC InChI with a modified algorithm that includes additional layers of chemical structure information. Consequently, yaChI string contains more structure details while preserving the original structure file information and can distinctively identify very closely related compounds reducing the chances of ambiguity in chemical compound databases as opposed to the general SMILES, InChI, and InChIKey. This study examines the relative performances of yaChI, SMILES, InChI, and InChIKey in duplication check for isomers. For simplicity, a small data set of 28 organometallic compounds (structural isomers of Rh-containing compounds) subdivided into three major groups (A, B, and C) based on the number and the type of ligands attached to the center atom was used to study the performances of each encoding scheme in describing chemical structures. SMILES, InChI, and InChIKey were generated using Openbabel and RDkit, whereas yaChI strings were generated with in-house program. Strings generated from SMILES, InChI, and InChIKey though different, resulted to only three unique chemical identifiers, with each belonging to one group indicating the presence of only three unique compounds in the study data. However, yaChI results depicted that all structures in each group are indeed unique and differ among themselves as well as those from other groups, mapping each structure with a unique identifier given a total number of 28 unique structures in the study data. This high perception of yaChI probe justifies its accuracy and reliability in duplication check among closely related compounds especially structures exhibiting stereo properties. [ABSTRACT FROM AUTHOR]

Since 2009 the Royal Society of Chemistry (RSC) has been delivering access to chemistry data and cheminformatics tools via the ChemSpider database and has garnered a significant community following in terms of usage and contribution to the platform. ChemSpider has focused only on those chemical entities that can be represented as molecular connection tables or, to be more specific, the ability to generate an InChI from the input structure. As a structure centric hub ChemSpider is built around the molecular structure with other data and links being associated with this structure. As a result the platform has been limited in terms of the types of data that can be managed, and the flexibility of its searches, and it is constrained by the data model. New technologies and approaches, specifically taking into account a shift from relational to NoSQL databases, and the growing importance of the semantic web, has motivated RSC to rearchitect and create a more generic data repository utilizing these new technologies. This article will provide an overview of our activities in delivering data sharing platforms for the chemistry community including the development of the new data repository expanding into more extensive domains of chemistry data. [ABSTRACT FROM AUTHOR]

ChEMBL, DrugBank, Human Metabolome Database and the Therapeutic Target Database are resources of curated chemistry-to-protein relationships widely used in the chemogenomic arena. In this work we have extended an earlier analysis (PMID 22821596) by comparing chemistry and protein target content between 2010 and 2013. For the former, details are presented for overlaps and differences, statistics of stereochemistry as well as stereo representation and MW profiles between the four databases. For 2013 our results indicate quality improvements, major expansion, increased achiral structures and changes in MW distributions. An orthogonal comparison of chemical content with different sources inside PubChem highlights further interpretable differences. Expansion of protein content by UniProt IDs is also recorded for 2013 and Gene Ontology comparisons for human-only sets indicate differences. These emphasise the expanding complementarity of chemistry-to-protein relationships between sources, although different criteria are used for their capture. [ABSTRACT FROM AUTHOR]

The article discusses research on MMsDusty as an International Chemical Identifier (InChI)-based tool to reduce chemical redundancy. It offers information on the construction and management process of a chemical database. It identifies the main operating core units of MMsDusty including InCHI-based orthography check, three-dimensional (3D) structure generation, and InCHI-based redundancy-elimination step.

Abstract: R27 is the prototype of the IncHI group of conjugative plasmids, which are associated with multidrug resistance in several relevant pathogens. The transfer of this plasmid is thermodependent and all transfer-related genes are encoded in six operons (tra operons). Very little is known about the factors involved in the regulation of the R27 conjugation. This report describes transcriptional studies of the six tra operons. Our results indicate that HtdA, encoded in the R27 plasmid, is involved in the transcriptional repression of four tra operons (F, H, AC and Z). Although HtdA plays a pivotal role in the transcriptional regulation of those operons, it does not exert its effect as a classical repressor. The data indicate the existence of a crosstalk between HtdA and other unknown regulatory factors. The HtdA-mediated regulation of conjugation is independent of the R27 H-NS protein. [Copyright &y& Elsevier]

Background: The InChI algorithms are written in C++ and not available as Java library. Integration into software written in Java therefore requires a bridge between C and Java libraries, provided by the Java Native Interface (JNI) technology. Results: We here describe how the InChI library is used in the Bioclipse workbench and the Chemistry Development Kit (CDK) cheminformatics library. To make this possible, a JNI bridge to the InChI library was developed, JNI-InChI, allowing Java software to access the InChI algorithms. By using this bridge, the CDK project packages the InChI binaries in a module and offers easy access from Java using the CDK API. The Bioclipse project packages and offers InChI as a dynamic OSGi bundle that can easily be used by any OSGi-compliant software, in addition to the regular Java Archive and Maven bundles. Bioclipse itself uses the InChI as a key component and calculates it on the fly when visualizing and editing chemical structures. We demonstrate the utility of InChI with various applications in CDK and Bioclipse, such as decision support for chemical liability assessment, tautomer generation, and for knowledge aggregation using a linked data approach. Conclusions: These results show that the InChI library can be used in a variety of Java library dependency solutions, making the functionality easily accessible by Java software, such as in the CDK. The applications show various ways the InChI has been used in Bioclipse, to enrich its functionality. [ABSTRACT FROM AUTHOR]

UniChem is a freely available compound identifier mapping service on the internet, designed to optimize the efficiency with which structure-based hyperlinks may be built and maintained between chemistry-based resources. In the past, the creation and maintenance of such links at EMBL-EBI, where several chemistry-based resources exist, has required independent efforts by each of the separate teams. These efforts were complicated by the different data models, release schedules, and differing business rules for compound normalization and identifier nomenclature that exist across the organization. UniChem, a large-scale, non-redundant database of Standard InChIs with pointers between these structures and chemical identifiers from all the separate chemistry resources, was developed as a means of efficiently sharing the maintenance overhead of creating these links. Thus, for each source represented in UniChem, all links to and from all other sources are automatically calculated and immediately available for all to use. Updated mappings are immediately available upon loading of new data releases from the sources. Web services in UniChem provide users with a single simple automatable mechanism for maintaining all links from their resource to all other sources represented in UniChem. In addition, functionality to track changes in identifier usage allows users to monitor which identifiers are current, and which are obsolete. Lastly, UniChem has been deliberately designed to allow additional resources to be included with minimal effort. Indeed, the recent inclusion of data sources external to EMBL-EBI has provided a simple means of providing users with an even wider selection of resources with which to link to, all at no extra cost, while at the same time providing a simple mechanism for external resources to link to all EMBL-EBI chemistry resources. [ABSTRACT FROM AUTHOR]

While chemical databases can be queried using the InChI string and InChIKey (IK) the latter was designed for openweb searching. It is becoming increasingly effective for this since more sources enhance crawling of their websites by the Googlebot and consequent IK indexing. Searchers who use Google as an adjunct to database access may be less familiar with the advantages of using the IK as explored in this review. As an example, the IK for atorvastatin retrieves ~200 low-redundancy links from a Google search in 0.3 of a second. These include most major databases and a very low false-positive rate. Results encompass less familiar but potentially useful sources and can be extended to isomer capture by using just the skeleton layer of the IK. Google Advanced Search can be used to filter large result sets. Image searching with the IK is also effective and complementary to open-web queries. Results can be particularly useful for less-common structures as exemplified by a major metabolite of atorvastatin giving only three hits. Testing also demonstrated document-to-document and document-to-database joins via structure matching. The necessary generation of an IK from chemical names can be accomplished using open tools and resources for patents, papers, abstracts or other text sources. Active global sharing of local IK-linked information can be accomplished via surfacing in open laboratory notebooks, blogs, Twitter, figshare and other routes. While information-rich chemistry (e.g. approved drugs) can exhibit swamping and redundancy effects, the much smaller IK result sets for link-poor structures become a transformative first-pass option. The IK indexing has therefore turned Google into a de-facto open global chemical information hub by merging links to most significant sources, including over 50 million PubChem and ChemSpider records. The simplicity, specificity and speed of matching make it a useful option for biologists or others less familiar with chemical searching. However, compared to rigorously maintained major databases, users need to be circumspect about the consistency of Google results and provenance of retrieved links. In addition, community engagement may be necessary to ameliorate possible future degradation of utility. [ABSTRACT FROM AUTHOR]

Abstract: Chemical formulations are compositions of active ingredients and inert formulation components (adjuvants), which are more effective in combination than the active ingredients alone. Thus, formulation technology synergistically can improve the properties of active ingredients with regards to various aspects like application, uptake, safety or storage. Chemical formulations have a broad scope of uses ranging from pharmaceuticals and agrochemicals to cosmetics and materials protection. Since they represent the product as it is finally brought to market they have a high commercial and patent value. Comprehensive search and retrieval of chemical formulations both in patent and non-patent literature builds the basis of a successful reinforcement of patent protection, avoidance of infringement of third party patent rights and competitive analysis. The paper discusses the various aspects of chemical formulations to be considered in chemical formulation searching, and presents best practice strategies for search, retrieval, and analysis of both patent and non-patent literature as well as internet content preferably related to agrochemical and pharmaceutical formulations. [Copyright &y& Elsevier]

A modified InChI (International Chemical Identifier) string scheme, yaInChI (yet another InChI), is suggested as a method for including the structural information of a given molecule, making it straightforward and more easily readable. The yaInChI theme is applicable for checking the structural identity with higher sensitivity and generating three-dimensional (3-D) structures from the one-dimensional (1-D) string with less ambiguity than the general InChI method. The modifications to yaInChI provide non-rotatable single bonds, stereochemistry of organometallic compounds, allene and cumulene, and parity of atoms with a lone pair. Additionally, yaInChI better preserves the original information of the given input file (SDF) using the protonation information, hydrogen count +1, and original bond type, which are not considered or restrictively considered in InChI and SMILES. When yaInChI is used to perform a duplication check on a 3D chemical structure database, Ligand.Info, it shows more discriminating power than InChI. The structural information provided by yaInChI is in a compact format, making it a promising solution for handling large chemical structure databases. [ABSTRACT FROM AUTHOR]

Background: Correctness of structures and associated metadata within public and commercial chemical databases greatly impacts drug discovery research activities such as quantitative structure-property relationships modeling and compound novelty checking. MOL files, SMILES notations, IUPAC names, and InChI strings are ubiquitous file formats and systematic identifiers for chemical structures. While interchangeable for many cheminformatics purposes there have been no studies on the inconsistency of these structure identifiers due to various approaches for data integration, including the use of different software and different rules for structure standardisation. We have investigated the consistency of systematic identifiers of small molecules within and between some of the commonly used chemical resources, with and without structure standardisation. Results: The consistency between systematic chemical identifiers and their corresponding MOL representation varies greatly between data sources (37.2%-98.5%). We observed the lowest overall consistency for MOL-IUPAC names. Disregarding stereochemistry increases the consistency (84.8% to 99.9%). A wide variation in consistency also exists between MOL representations of compounds linked via cross-references (25.8% to 93.7%). Removing stereochemistry improved the consistency (47.6% to 95.6%). Conclusions: We have shown that considerable inconsistency exists in structural representation and systematic chemical identifiers within and between databases. This can have a great influence especially when merging data and if systematic identifiers are used as a key index for structure integration or cross-querying several databases. Regenerating systematic identifiers starting from their MOL representation and applying well-defined and documented chemistry standardisation rules to all compounds prior to creating them can dramatically increase internal consistency. [ABSTRACT FROM AUTHOR]

Background: There are two line notations of chemical structures that have established themselves in the field: the SMILES string and the InChI string. The InChI aims to provide a unique, or canonical, identifier for chemical structures, while SMILES strings are widely used for storage and interchange of chemical structures, but no standard exists to generate a canonical SMILES string. Results: I describe how to use the InChI canonicalisation to derive a canonical SMILES string in a straightforward way, either incorporating the InChI normalisations (Inchified SMILES) or not (Universal SMILES). This is the first description of a method to generate canonical SMILES that takes stereochemistry into account. When tested on the 1.1 m compounds in the ChEMBL database, and a 1 m compound subset of the PubChem Substance database, no canonicalisation failures were found with Inchified SMILES. Using Universal SMILES, 99.79% of the ChEMBL database was canonicalised successfully and 99.77% of the PubChem subset. Conclusions: The InChI canonicalisation algorithm can successfully be used as the basis for a common standard for canonical SMILES. While challenges remain - such as the development of a standard aromatic model for SMILES - the ability to create the same SMILES using different toolkits will mean that for the first time it will be possible to easily compare the chemical models used by different toolkits. [ABSTRACT FROM AUTHOR]

The simplified molecular input-line entry system (SMILES) and IUPAC International Chemical Identifier (InChI) were examined as representations of the molecular structure for quantitative structure-activity relationships (QSAR), which can be used to predict the inhibitory activity of styrylquinoline derivatives against the human immunodeficiency virus type 1 (HIV-1). Optimal SMILES-based descriptors give a best model with n = 26, r = 0.6330, q = 0.5812, s = 0.502, F = 41 for the training set and n = 10, r = 0.7493, = 0.6235, = 0.537, s = 0.541, F = 24 for the validation set. Optimal InChI-based descriptors give a best model with n = 26, r = 0.8673, q = 0.8456, s = 0.302, F = 157 for the training set and n = 10, r = 0.8562, = 0.7715, = 0.819, s = 0.329, F = 48 for the validation set. Thus, the InChI-based model is preferable. The described SMILES-based and InChI-based approaches have been checked with five random splits into the training and test sets. InChI-based optimal descriptors give more robust prediction for anti-HIV-1 activity (pEC50) than analogical descriptors calculated with SMILES. [ABSTRACT FROM AUTHOR]

Abstract: The International Chemical Identifier (InChI) has been used to construct InChI-based optimal descriptors to model the binding affinity for fullerene[C60]-based inhibitors of human immunodeficiency virus type 1 aspartic protease (HIV-1 PR). Statistical characteristics of the one-variable model obtained by the balance of correlations are as follows: n =8, r 2 =0.9769, q 2 LOO =0.9646, s =0.099, F =254 (subtraining set); n =7, r 2 =0.7616, s =0.681, F =16 (calibration set); n =5, r 2 =0.9724, s =0.271, F =106, R m 2 =0.9495 (test set). Predictability of this approach has been checked with three random splits of the data: into the subtraining set, calibration set, and test set. [Copyright &y& Elsevier]

Optimal descriptors calculated with international chemical identifier have been used to construct one-variable model of the normal boiling points of acyclic carbonyl substances. Attempts to calculate the model for three splits into training and test sets gave stable results. Statistical quality of the model is n = 150, r2 = 0.9825, s = 4.96 °C, F = 8,312 (training set) and n = 50, r2 = 0.9791, s = 4.68 °C, F = 2,249 (test set). [ABSTRACT FROM AUTHOR]

Abstract: In depth analysis of non-patent literature prior art is a crucial step in checking patentability of new inventions and validity of competitor’s patents, since by patent law relevant subject matter disclosed in non-patent literature is as important as any patent document. E-journal articles, as well as any scientific and technical information published on the web are an important source of prior art that is very often insufficiently covered and indexed by commercial databases. This article reviews search and display capabilities of e-journal search sites of different publishers and hosts, as well as their value for full-text prior art analysis to enhance retrieval from commercial databases. Moreover, current developments and future prospects of chemical structure searching both in e-journals and on the internet are discussed. [Copyright &y& Elsevier]

Optimal descriptors calculated with International Chemical Identifier (InChI) have been used to construct one-variable model of the solubility of fullerene C60 in organic solvents . Attempts to calculate the model for three splits into training and test sets gave stable results. Statistical quality of the model is n = 92, r2 = 0.9447, Q2 = 0.9418, s = 0.253, standard deviation of error of prediction (SDEP) = 0.258, F = 1, 538 (training set) and n = 30, r2 = 0.9398, $${R^{2}_{\rm pred}=0.9315}$$ , s = 0.348, F = 437 (test set). [ABSTRACT FROM AUTHOR]

Comparison of the quantitative structure—property relationships (QSPR) based on optimal descriptors calculated with the International Chemical Identifier (InChI) and QSPR based on optimal descriptors calculated with simplified molecular input line entry system has shown that the InChI-based optimal descriptors give more accurate prediction for the logarithm of octanol/water partition coefficient of platinum complexes. [ABSTRACT FROM AUTHOR]

A structural diagram, in the form of a two-dimensional (2-D) sketch, remains the most effective portrait of a "small molecule" or chemical reaction. However, such structural diagrams, as for any other core data, cannot be used in speech (and should not be used in free text). "Good annotation practice" for biological databases is to use either consistent and widely recognised terminology or unique identifiers from a dedicated database to refer to the molecule of interest. Ideally, scientists should use terminology that is both pronounceable and meaningful. Thus, a viable solution for a bioinformatician is to use a definitive controlled vocabulary of biochemical compounds and reactions, which contains both systematic and common names. In addition, chemical ontologies provide a means for placing entities of interest into wider chemical, biological or medical contexts. We present some challenges and achievements in the standardisation of chemical language in biological databases, with emphasis on three aspects of annotation: 1. good drawing practice: how to draw unambiguous 2-D diagrams; 2. good naming practice: how to give most appropriate names; and 3. good ontology practice: how to link the entity of interest by defined logical relationships to other entities. [ABSTRACT FROM AUTHOR]

Fifty-eight clinical Salmonella typhi strains isolated from patients suspected of suffering from typhoid fever were obtained at the Korle-Bu Teaching Hospital and the Noguchi Memorial Institute for Medical Research, both located in Ghana, Africa. Each isolate was examined for susceptibility to ampicillin, chloramphenicol, streptomycin, tetracycline, and trimethoprim/sulfamethoxazole by the disk diffusion assay. Five of the isolates were resistant to all five antibiotics while 10 isolates were resistant to ampicillin, chloramphenicol, and trimethoprim/sulfamethoxazole, which are considered ‘first line’ antibiotics in the treatment of typhoid fever. Thirty-four isolates were resistant to at least one of the antibiotics tested and 62% of these isolates possessed conjugable plasmids belonging to incompatibility group IncHI. Ninety percent of the conjugable plasmids conferred a multiple drug-resistant phenotype on the strains harboring them. Additionally, 14 strains contained plasmids that were transformable and six of them encoded multiple drug resistance. Our findings indicate that multiple drug resistance to the ‘first line’ antibiotics in S. typhi may be more prevalent in Africa than previously thought. [Copyright &y& Elsevier]