Representation Of Chemical Structures Research Articles

The Status of the IUPAC InChI Chemical Structure Standard

Abstract For almost 100 years, IUPAC has been well known around the world for its efforts in standardizing nomenclature in chemistry. At the start of the present century, it became clear to all involved in chemical structure representation work that, with the extensive use of computers and electronic information in all aspects of chemistry and related sciences, an IUPAC standard was necessary. From this critical need, the IUPAC International Chemical Identifier—InChI—project was launched in cooperation with the US standards agency NIST. The result of this effort has been the development, maintenance, and expansion of capabilities of the open source nonproprietary International Chemical Identifier (InChI), first by NIST and now by the InChI Trust, a not-for-profit UK charity. Over 100 chemical information specialists and computational chemists volunteer to test the software before a public release; this optimal quality control by a world-wide user community has led to improvements to and releases of the software with very few problems. The reliance on input from many volunteers enables the project staff to be restricted to two part-time contractors, a project director and a programmer, thus minimizing the running costs of the Trust.

ChemSAR: an online pipelining platform for molecular SAR modeling

BackgroundIn recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers.ResultsThis work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files.ConclusionChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.Graphical abstract.

Milan Randic, Marjana Novic, and Dejan Plavsic: Solved and Unsolved Problems of Structural Chemistry

problems may not appear gripping, some even ignorable or esoteric, but it is not hard work to follow the general thread of an argument. In that regard, it provides some traction for the ideas that keep theoreticians awake at night while rarely disturbing the sleep of experimental chemists. Since the topic of this book is a relatively new field (at least in terms of applications), the current book is comprehensive concerning the ideas in play in that field. The authors hope to excite the general scientific community into engaging and applying chemical graph theory in a broader sense to chemical theory. They feel that in the past, they have lacked the support of traditional theoretical chemists and devote a significant space to reputing these opinions. Unless soap operas are of particular interest, those further removed from the discipline, and would suggest that the waring fractions get over it and move on. To me, this was the least worthwhile aspect of the book, but obviously a sensitive issue for the authors. Unless you are particularly interested in theoretical chemistry, it is difficult to see how this book (or probably any other) will attract you into the fold. Fortunately, science is made up of many specialties and allows scientists with different interests to participate in its evolution. The authors may succeed in persuading some of a like mind that discrete mathematics and graph theory represent a new insight into chemical structural problems, but outside this group, converts are likely to be hard to find. For those with a strong interest in the topic of the book, there are some positive attributes to justify purchase. The book is foremost readable, copiously and suitably illustrated, and set out in a logical manner. I found the index to be well prepared, and for most of my needs had no difficulty navigating through the material using the table of contents. Seminal work is adequately cited. The 23 appendices help to keep the general text from getting too dense This book explores the development of discrete mathematics, quantum physics, and chemical graph theory to problems related to quantitative structure–property relationships and graphical bioinformatics. Structural chemistry in this case is the representation of chemical structures by a mathematical description validated by the ability to quantitatively describe some chemical property or feature. It is a book mainly about theory but written in an interesting way. It covers the history and context in which graph theory (largely) was developed, and the challenge in gaining acceptance from theoretical physical chemists. There are remarkably a few equations for a book of this type, and the authors attempt to make their case through the media of text and figures. Since most general readers will find the problems alluded to in the title focused on the abstract side of chemistry, the text is remarkably readable in a story book style. Unless you are a true physical chemist, these

Nitroxide radical containing nanoparticle as a potential candidate for redox therapeutics in breast cancer

Event Abstract Back to Event Nitroxide radical containing nanoparticle as a potential candidate for redox therapeutics in breast cancer Babita Shashni1 and Yukio Nagasaki1, 2, 3 1 Graduate School of Pure and Applied Sciences, University of Tsukuba, Department of Material Science, Japan 2 Graduate School of Comprehensive Human Sciences, University of Tsukuba, Master’s School of Medical Sciences, Japan 3 National Institute for Materials Science (NIMS), Satellite Laboratory, International Center for Materials Nanoarchitectonics (WPI-MANA), Japan Introduction: Elevated basal level of reactive oxygen species (ROS) is a hallmark for cancer cell phenotype. ROS, which is considered to be an oncogenic, has been reported to play a critical role in cancer development, progression and metastasis. Therefore ROS manipulating strategies in cancers may hold a great potential for cancer therapy. Henceforth, in this line we focused our study to investigate the therapeutic efficacy of our pH sensitive and non-sensitive ROS scavenging redox nanoparticle (RNP) [1],[2] (Figure 1) in breast cancer by specific inhibition of ROS regulated proteins associated with cancer metastasis and angiogenesis. Figure 1: Chemical structure representation of a) RNP(N): pH-sensitive and b) RNP(O): non-pH sensitive Materials and Methods: Therapeutic efficacy of RNPs was evaluated by in vitro and in vivo assays. In vitro anti-proliferative, -metastatic and -angiogenic activities were confirmed by cell viability, migration and endothelial migration and tube cell formation assays, respectively, whereas in in vivo the efficacy was investigated in breast cancer mice models by biochemical assays. The mechanisms of anti -metastatic and -angiogenic activities were investigated by expression studies. Results: We confirmed that RNPs significantly restricts proliferation, migration and angiogenesis in breast cancer. Furthermore, we also confirmed RNP directed suppression of ROS regulated migration (NF-kβ and metallo matrix proteases (MMPs)) and angiogenesis (vascular endothelial growth factor (VEGF)) promoting proteins. Anti-metastatic tendency was also confirmed in in vivo breast cancer mice models. Discussion: ROS plays a critical role in cancer cell survival. Considering the dependence of cancer cells on ROS-induced molecular signaling pathways, ROS can thus represent a vulnerable specific target for redox cancer therapeutics. In this study we specifically inhibited the ROS induced metastasis and angiogenesis promoting characteristics of breast cancer cells by down-regulating transcriptional factor NF-kB, MMPs and VEGF (Figure 2). This preclinical evaluation study will thus establish RNP as a potential candidate for breast cancer therapeutics. Figure 2: Schematic representation of mechanism of RNP directed restriction of metastasis and angiogenesis in breast cancer Conclusion: We provide the first molecular evidence of anti-metastatic and anti-angiogenic activities of RNP. This report thus strengthens the role of ROS scavenging nanoparticles as potential candidates for breast cancer therapeutics.

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

BackgroundIn silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process.Resultscamb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2).Conclusions Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0086-2) contains supplementary material, which is available to authorized users.

Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling

Proteochemometrics (PCM) is a predictive bioactivity modelling method to simultaneously model the bioactivity of multiple ligands against multiple targets. Therefore, PCM permits to explore the selectivity and promiscuity of ligands on biomolecular systems of different complexity, such proteins or even cell-line models. In practice, each ligand-target interaction is encoded by the concatenation of ligand and target descriptors. These descriptors are then used to train a single machine learning model. This simultaneous inclusion of both chemical and target information enables the extra- and interpolation to predict the bioactivity of compounds on targets, which can be not present in the training set. In this thesis, a methodological advance in the field is firstly introduced, namely how Bayesian inference (Gaussian Processes) can be successfully applied in the context of PCM for (i) the prediction of compounds bioactivity along with the error estimation of the prediction; (ii) the determination of the applicability domain of a PCM model; and (iii) the inclusion of experimental uncertainty of the bioactivity measurements. Additionally, the influence of noise in bioactivity models is benchmarked across a panel of 12 machine learning algorithms, showing that the noise in the input data has a marked and different influence on the predictive power of the considered algorithms. Subsequently, two R packages are presented. The first one, Chemically Aware Model Builder (camb), constitues an open source platform for the generation of predictive bioactivity models. The functionalities of camb include : (i) normalized chemical structure representation, (ii) calculation of 905 one- and two-dimensional physicochemical descriptors, and of 14 fingerprints for small molecules, (iii) 8 types of amino acid descriptors, (iv) 13 whole protein sequence descriptors, and (iv) training, validation and visualization of predictive models. The second package, conformal, permits the calculation of confidence intervals for individual predictions in the case of regression, and P values for classification settings. The usefulness of PCM to concomitantly optimize compounds selectivity and potency is subsequently illustrated in the context of two application scenarios, which are: (a) modelling isoform-selective cyclooxygenase inhibition; and (b) large-scale cancer cell-line drug sensitivity prediction, where the predictive signal of several cell-line profiling data is benchmarked (among others): basal gene expression, gene copy-number variation, exome sequencing, and protein abundance data. Overall, the application of PCM in these two case scenarios let us conclude that PCM is a suitable technique to model the activity of ligands exhibiting uncorrelated bioactivity profiles across a panel of targets, which can range from protein binding sites (a), to cancer cell-lines (b).

Analysis and synthesis in olfaction.

Natural environments contain numerous volatile compounds emanating from a large number of sources, and the survival of many animals depends on their ability to segregate odors of interest within complex odorous scenes. In a recent paper, we described how the ability of mice to detect odors within mixtures depends on the chemical structure and neural representation of the target and background odorants.

Full-motion videos: Bringing abstract chemical concepts to life in the classroom

A set of full-motion videos utilizing two-dimensional images and representations of three-dimensional chemical structures was made using a software suite, the Molecular Operating Environment (MOE). The videos highlight the drug’s structure–activity relationship (SAR) and their corresponding receptor interactions. The full-motion video set was created for use as a supplemental teaching aid for a medicinal chemistry course. The literature describes using full-motion videos in undergraduate courses, such as organic chemistry and biochemistry; however, there is no demonstration of movies being employed in pharmacy chemistry-based courses such as medicinal chemistry. Anecdotal feedback and unsolicited student comments on teaching evaluations from pharmacy students directly indicated increased student understanding and comfort with the course content. Student feedback has motivated the authors to develop a future study to purposefully assess if there is a benefit to using the videos in comparison to static 2D diagrams as a supplement to the medicinal chemistry lectures.

ChemInform Abstract: Current Trends in the Development of A.M. Butlerov′s Theory of Chemical Structure

AbstractReview: 117 refs.

Chemical Graphs, Molecular Matrices and Topological Indices in Chemoinformatics and Quantitative Structure-Activity Relationships§

Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

Challenges in mining the literature for chemical information

Chemical information extracted from the literature is of immense value for the pharmaceutical and chemical industries in many areas, including supporting drug discovery, manufacturing processes, or intellectual property protection. However, the exponential growth of the chemical literature has made it increasingly difficult for researchers to find the information they need within a reasonable time-frame. In order to address this issue, a large number of text mining approaches have been developed that can extract chemical information from different types of literature. But the lack of a single universal standard for chemical structure and nomenclature representation has posed significant challenges in mining the chemical information. Hence, a review on the current state of chemical text mining, problems confronted, solutions available, and future prospectus is presented.

Design and implementation of a next-generation software interface for on-the-fly quantum and force field calculations in automated reaction mechanism generation

A software interface for performing on-the-fly quantum and force field calculations has been developed and integrated into RMG, an open-source reaction mechanism generation software package, to provide needed estimates of thermodynamic parameters. These estimates based on three-dimensional molecular geometries bypasses the traditional group-additivity-based approach, which can suffer from lack of availability of necessary parameters; this issue is particularly evident for polycyclic species with fused rings, which would require ad hoc ring corrections in the group-additivity framework. In addition to making extensive use of open-source tools, the interface takes advantage of recent developments from several fields, including three-dimensional geometry embedding, force fields, and chemical structure representation, along with enhanced robustness of quantum chemistry codes. The effectiveness of the new approach is demonstrated for a computer-constructed model of combustion of the synthetic jet fuel JP-10. The interface also establishes a framework for future improvements in the chemical fidelity of computer-generated kinetic models.

Current trends in the development of A. M. Butlerov’s theory of chemical structure

The chemical structure concept developed by A. M. Butlerov supplemented by the views on spatial (J. H. van’t-Hoff and J. A. Le Bel) and electronic (G. Lewis) configurations of molecules constitute the basis of the classical theory of chemical structure. The advent of quantum mechanics and development of the computer chemistry extended and enhanced the conceptual basis of theoretical chemistry, which nevertheless retains its independent value and cannot be reduced to direct physical definitions. The review deals with the evolution of the key concepts of the classical theory of chemical structure and introduction of new notions and approaches to analysis of the structure and reactivity problems, which is associated with the advent of the quantum mechanics and quantum chemistry views and methods.

International congress on organic chemistry dedicated to the 150th year anniversary of A. M. Butlerov’s theory of chemical structure of organic compounds

International congress on organic chemistry dedicated to the 150th year anniversary of A. M. Butlerov’s theory of chemical structure of organic compounds

Representation of chemical structures

Representation of chemical structures

Ketcher: web-based chemical structure editor

Ketcher is an open-source web-based chemical structure editor developed by SciTouch LLC. Being written on pure JavaScript it incorporates high performance, good portability and light weight. Editor supports late versions of all most popular browsers, such as Internet Explorer, Firefox, Safari, Opera and Chrome. It is developed with minimum third-party code to remain its light weight. It is platform-independent and can be used as a structure viewer as well. SciTouch keeps on working to add more features to Ketcher in near future. Ketcher is available under Affero GPL license. Features: • Standalone mode. Ketcher supports standalone mode in which no server support is required. • Scalable Vector Graphics (SVG) for rendering. Ketcher uses SVG to achieve the best quality of in-browser chemical structure rendering. SVG standard is supported by most of modern browsers and provides smooth and light-weight drawing. IE rendering is based on VML (Vector Markup Language) instead. • SMILES strings. SMILES is a compact format for chemical structure representation. Ketcher gives you ability to load and save structure in this useful format. • Automatic layout (clean up). Server-side structure layout is implemented basing on Indigo cheminformatics library with a Python wrapper. Indigo provides fast 2D structure representation which satisfies common chemical drawing standards. • And also: MDL (Symyx) molfiles support, hot keys, stereochemistry support, complete undo/redo history and much more. Ketcher web site: http://scitouch.net/ketcher Try Ketcher demo: http://scitouch.net/ketcher-demo/ketcher.html

Representation of chemical structures

AbstractAt the root of applications for substructure and similarity searching, reaction retrieval, synthesis planning, drug discovery, and physicochemical property prediction is the need for a machine‐readable representation of a structure. Systematic nomenclature is unsuitable, and notations and fragment codes have been superseded, except in certain specific applications. Connection tables are widely used, but there is no formal standard. Recently the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) has started to attract interest. This review also summarizes the representation of chemical reactions and three‐dimensional structures. © 2011 John Wiley & Sons, Ltd. WIREs Comput Mol Sci 2011 1 557–579 DOI: 10.1002/wcms.36This article is categorized under: Computer and Information Science > Chemoinformatics

Study on Coal Spontaneous Combustion Characteristic Structures Affected by Ionic Liquids

According to modern coal chemical structure theory, the active chemical structure categories in coal spontaneous combustion process were summed up, which show that the coal spontaneous combustion characteristic structures are dynamic, such as methyl, methylene, hydroxyl, carbonyl and others in coal bridge bonds and side chains. The amount and activity of these structures play an important role in coal low temperature oxidation stage. Based on this, the article analyzed the effect of ionic liquids to coal spontaneous combustion characteristic structures, which indicates that the structure amount of coal affected by ionic liquids decreases obviously compared with raw coal.

Molecular fragments chemoinformatics

The description of chemical structures as a collection of connected molecular fragments is a basic requirement of coarse grained simulation methods like molecular fragment dynamics. These methods use molecular fragments as their basic interacting entities (atoms) and allow the modelling and investigation of very large chemical systems. Therefore a molecular fragments chemoinformatics is in need that supports the fragment-based representation of chemical structures as well as the elementary operations upon them. The poster outlines definitions and approaches to tackle these issues from an adequate molecular line notation up to the graphical representation of simulation boxes.

A method for phenomenological calculations in series of substituted benzenes

The methodology of calculations of the physicochemical properties of substituted benzenes C6H6 − pXp, C6H6 − p − qXpYq, ... (X and Y are univalent substituents) and C6H5A, where A = OH (phenol), NH2 (aniline), etc. by the phenomenological methods of chemical structure theory is discussed.