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The purpose of this paper is to predict the aquatic toxicity (LC50) of 92 substituted benzenes derivatives in Pimephales promelas.

Quantitative structure-activity relationship analysis was performed on a series of 92 substituted benzenes derivatives using multiple linear regression (MLR), artificial neural network (ANN) and support vector machines (SVM) methods, which correlate aquatic toxicity (LC50) values of these chemicals to their structural descriptors. At first, the entire data set was split according to Kennard and Stone algorithm into a training set (74 chemicals) and a test set (18 chemical) for statistical external validation.

Models with six descriptors were developed using as independent variables theoretical descriptors derived from Dragon software when applying genetic algorithm – variable subset selection procedure.

The values of Q2 and RMSE in internal validation for MLR, SVM, and ANN model were: (0.8829; 0.225), (0.8882; 0.222); (0.8980; 0.214), respectively and also for external validation were: (0.9538; 0.141); (0.947; 0.146); (0.9564; 0.146). The statistical parameters obtained for the three approaches are very similar, which confirm that our six parameters model is stable, robust and significant.

The purpose of this article is to analyse the literature of chemoinformatics, a subject that has arisen over the last few years and that draws on techniques from a range of disciplines, most notably chemistry (particularly computational and medicinal chemistry), computer science and information science.

Discusses subject, author and citation searches of (principally) the web of knowledge database.

The Journal of Chemical Information and Modeling (previously the Journal of Chemical Information and Computer Sciences) is the core journal for the subject, but with many significant papers being published in journals whose principal focus is molecular modelling, quantitative structure‐activity relationships or more general aspects of chemistry. The discipline is international in scope, and many of the most cited papers describe software packages that play a key role in modern chemoinformatics research.

This is the first bibliometric study of chemoinformatics, and one of only a very few that consider the bibliometrics of computational chemistry more generally.

The purpose of this paper is to predict the retention times of 84 pesticides or toxicants.

Quantitative structure – retention relationship analysis was performed on a set of 84 pesticides or toxicants using a hybrid approach genetic algorithm/multiple linear regression (GA/MLR).

A model with six descriptors was developed using as independent variables. Theoretical descriptors derived from Spartan and Dragon softwares when applying GA/MLR approach.

A six parameter linear model developed by GA/MLR, with R² of 90.54, Q² of 88.15 and S of 0.0381 in Log value. Several validation techniques, including leave-many-out cross-validation, randomization test, and validation through the test set, illustrated the reliability of the proposed model. All of the descriptors involved can be directly calculated from the molecular structure of the compounds, thus the proposed model is predictive and could be used to estimate the retention times of pesticides or toxicants.

Using science mapping analysis approach and co-word analysis, the present study explores and visualizes research fields and thematic evolution of the coronavirus. Based on this method, one can get a picture of the real content of the themes in the mentioned thematic area and identify the main minor and emerging themes.

This study was conducted based on co-word science mapping analysis under a longitudinal study (from 1988 to 2020). The collection of documents in this study was further divided into three subperiods: 1988–1998, 1999–2009 and 2010–2020. In order to perform science mapping analysis based on co-word bibliographic networks, SciMAT was utilized as a bibliometric tool. Moreover, WoS, PubMed and Scopus bibliographic databases were used to download all records.

In this study, strategic diagrams were demonstrated for the coronavirus research for a chronological period to assess the most relevant themes. Each diagram depended on the sum of documents linked to each research topic. In the first period (1988–1998), the most centralizations were on virology and evaluation of coronavirus structure and its structural and nonstructural proteins. In the second period (1999–2009), with due attention to high population density in eastern Asia and the increasing number of people affected with the new generation of coronavirus (named severe acute respiratory syndrome virus or SARS virus), publications have been concentrated on “antiviral activity.” In the third period (2010–2020), there was a tendency to investigate clinical syndromes, and most of the publications and citations were about hot topics like “severe acute respiratory syndrome,” “coronavirus” and “respiratory tract disease.” Scientometric analysis of the field of coronavirus can be regarded as a roadmap for future research and policymaking in this important area.

The originality of this research can be considered in two ways. First, the strategic diagrams of coronavirus are drawn in four thematic areas including motor cluster, basic and transversal cluster, highly developed cluster and emerging and declining cluster. Second, COVID-19 is mentioned as a hot topic of research.

This study aims to investigate the inhibition performance of an aqueous extract of Matricaria recutita chamomile on the corrosion of S235JR steel in 0.5 M NaCl by using electrochemical impedance spectroscopy (EIS) and polarization measurements.

The inhibition performance was investigated using EIS and polarization measurements. Surface analysis demonstrates the presence of a protective layer on the steel surface in the presence of the extract. Quantum chemical parameters calculated for the molecules contained in the aqueous extract are interpreted to predict the corrosion inhibition efficiency of the considered extract.

The inhibition efficiency of chamomile aqueous extract for S235JR steel increases with increasing amounts of plant concentration and with an increase in the immersion time. The optimal inhibition efficiency of chamomile extract, 98.90 per cent, was achieved for S235JR steel when immersed in 15 per cent v/v of extract concentration for 2 h. The surface analysis in the absence and presence of the chamomile extract confirmed the formation of a protective layer on steel surface. The quantum chemical calculations allowed to explain the great inhibition efficiency values by interpreting the calculated quantum parameters.

This is the first study carrying out an experimental and theoretical investigation on M. recutita chamomile as a green corrosion inhibitor, with interesting potential industrial applications.

The study aims to add to the body of knowledge of open source tangible product management (also called open design). The objective is also to develop a guideline for efficient open source tangible product development and adoption.

The exploratory research design using secondary data (like newspapers, magazines, research articles, bogs, papers, etc.) is used to analyze open source tangible product design challenges and enablers. The success stories of Open Source Software projects (OSS) were studied for identification of critical success factors and further their relevancy was tested in the two popular cases of open source drug discovery (malaria and tuberculosis)

Open innovation has become a part of competitive strategy of current businesses. It requires an efficient intellectual property protection regime for its implementation. However, in a market dominated by proprietary benefits, the open source technology development can serve as remedy for innovation needs of neglected sectors. The OSS literature revealed managing two classes of factors, namely technology sponsor level factors and environmental factors for efficiency and effectiveness. The case study analysis in the context of applicability of these OSS critical factors showed their limitations in open source tangible products, and highlighted understanding additional challenges and remedies.

Open source innovation is a collaborative effort involving inputs from various/diverse players, hence monitoring the effort and motivation level of the contributors is a cumbersome task. Only the information that is available online and in print media is taken as research inputs in this work. Also the data taken were from two case studies; a lot more case studies in the open design domain can progress the theory. The implications of this study are far-reaching in the areas where profit motivated proprietary efforts lack in addressing societal need. It provides guidelines for addressing those unmet needs by developing products in a collaborative way without intellectual property hurdles.

The essence of open design is becoming more vital, and there is a pressing need to build theory to support it, which still is elusive and dispersed. The study fills the gap using secondary data and case study approach.

This is a speculative paper in which the requirements of IR systems to support relatively creative, as well as more convergent thinking are discussed. The nature of creative thinking is explored, as is the extent to which a range of current information systems is able to support key intellectual processes associated with it. The development of IR systems capable of providing more direct support for creative thinking will depend on the greater integration of high order knowledge representations and flexible, fuzzy pattern‐matching techniques. Such developments may enhance the ability of information seekers to place before themselves a range of information sufficiently – but not excessively – rich in diversity to facilitate the development of relatively divergent – as well as more convergent – ideas.

The purpose of this paper is to predict the octanol/water partition coefficient (K_ow) of 43 organophosphorous compounds.

A quantitative structure-property relationship analysis was performed on a series of 43 pesticides using multiple linear regression and support vector machines methods, which correlate the octanol-water partition coefficient (K_ow) values of these chemicals to their structural descriptors. At first, the data set was randomly separated into a training set (34 chemicals) and a test set (nine chemicals) for statistical external validation.

Models with three descriptors were developed using theoretical descriptors as independent variables derived from Dragon software while applying genetic algorithm-variable subset selection procedure.

The robustness and the predictive performance of the proposed linear model were verified using both internal and external statistical validation. One influential point which reinforces the model and an outlier were highlighted.

The large number and big variety of online databases in the field of environmental sciences and chemistry underlines the need for a comparative evaluation approach. In this paper 12 evaluation criteria are presented. The criteria are divided into: • general criteria: SI (size of the data‐source), CO (cost of one hour online searching), UP (update of online database), and AV (availability on other media) • chemical‐relevant criteria: NU (number of chemicals), ID (identification parameter for chemicals), CT (testset chemicals), CD (development of chemicals) • environmental‐relevant criteria: IP (information parameters for chemical substances), PD (parameter development) and • criteria describing environmental chemicals: US (use of chemical substances), QU (quality of database). A six number scoring system is applied on these criteria. Furthermore a comparative evaluation approach, the so‐called Hasse‐diagram‐technique is presented for 19 bibliographic online databases using the criteria mentioned above. In this approach maximals (‘good’ databases) and minimals (‘bad’ databases) can be identified, for example. Using the Hasse‐diagram‐technique changes in the database content from 1995 to 1998 applied on the 19 databases can be visualised.