Special Issue "Chemometrics in Assessing Molecular Structures and Properties"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Chemistry and Symmetry/Asymmetry".

Deadline for manuscript submissions: 15 May 2021.

Special Issue Editor

Prof. Dr. Vasil D. Simeonov
E-Mail Website
Guest Editor
Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., Sofia 1126, BULGARIA
Interests: chemometrics; environmetrics; multivariate calibration; classification, interpretation and modeling of environmental data sets; evaluation and optimization of analytical procedures; potentiometry with ion selective electrodes; atmospheric and marine chemistry

Special Issue Information

Dear Colleagues,

The significance of chemometrics as a reliable strategy not only for intelligent data analysis, but also as a tool for modeling, classification, and interpretation of large data sets for bioinformatics studies is constantly growing. The major goal of the present Special Issue of Symmetry on chemometrics application is to offer an open access platform for scientists using various statistical and computational methods in bioinformatics, omics studies, system biology, structural/properties relationships, environmental monitoring issues, and clinical studies.

Prof. Dr. Vasil D. Simeonov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • chemometrics
  • bioinformatics
  • QSPR
  • databases
  • modeling
  • classification

Published Papers (4 papers)

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Research

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Open AccessArticle
Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors
Symmetry 2020, 12(11), 1763; https://doi.org/10.3390/sym12111763 - 24 Oct 2020
Cited by 1 | Viewed by 347
Abstract
The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different [...] Read more.
The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects’ groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents. Full article
(This article belongs to the Special Issue Chemometrics in Assessing Molecular Structures and Properties)
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Open AccessArticle
Multivariate Chemometrics as a Strategy to Predict the Allergenic Nature of Food Proteins
Symmetry 2020, 12(10), 1616; https://doi.org/10.3390/sym12101616 - 29 Sep 2020
Cited by 1 | Viewed by 392
Abstract
The purpose of the present study is to develop a simple method for the classification of food proteins with respect to their allerginicity. The methods applied to solve the problem are well-known multivariate statistical approaches (hierarchical and non-hierarchical cluster analysis, two-way clustering, principal [...] Read more.
The purpose of the present study is to develop a simple method for the classification of food proteins with respect to their allerginicity. The methods applied to solve the problem are well-known multivariate statistical approaches (hierarchical and non-hierarchical cluster analysis, two-way clustering, principal components and factor analysis) being a substantial part of modern exploratory data analysis (chemometrics). The methods were applied to a data set consisting of 18 food proteins (allergenic and non-allergenic). The results obtained convincingly showed that a successful separation of the two types of food proteins could be easily achieved with the selection of simple and accessible physicochemical and structural descriptors. The results from the present study could be of significant importance for distinguishing allergenic from non-allergenic food proteins without engaging complicated software methods and resources. The present study corresponds entirely to the concept of the journal and of the Special issue for searching of advanced chemometric strategies in solving structural problems of biomolecules. Full article
(This article belongs to the Special Issue Chemometrics in Assessing Molecular Structures and Properties)
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Open AccessArticle
Chemometric Evaluation of the Link between Acute Toxicity, Health Issues and Physicochemical Properties of Silver Nanoparticles
Symmetry 2019, 11(9), 1159; https://doi.org/10.3390/sym11091159 - 11 Sep 2019
Cited by 2 | Viewed by 729
Abstract
The present study’s objective is to focus on some developments in the field of statistical models of a complex system, like nanoparticles responses in the environmental media. An important problem that still needs to be studied and interpreted is the relations between physicochemical [...] Read more.
The present study’s objective is to focus on some developments in the field of statistical models of a complex system, like nanoparticles responses in the environmental media. An important problem that still needs to be studied and interpreted is the relations between physicochemical parameters of the nanoparticles like primary size, primary hydrophobic diameter, zeta potential, etc. with respective toxicity values. It holds true especially for silver nanoparticle systems due to their known bactericidal effect and wide distribution in practice. The present study deals with the data for physicochemical and toxicity parameters of 94 different silver nanoparticle systems in order to reveal specific relations between physicochemical properties and acute toxicity readings using multivariate statistical methods. Searching for these specific relationships between physicochemical parameters and toxicity responses is the novel element in the present study. This has focused our study toward developing a model that describes the relationship between physicochemical properties and toxicity of silver NPs based on a dataset gathered from the literature. It is shown that the systems studied could be divided into four patterns (clusters) of similarity depending not only on the physicochemical indicators related to particles size but also by their acute toxicity. The acute toxicity is strongly correlated to the zeta potential of the particles if the whole data set is considered. Full article
(This article belongs to the Special Issue Chemometrics in Assessing Molecular Structures and Properties)
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Review

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Open AccessReview
Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review
Symmetry 2020, 12(12), 2055; https://doi.org/10.3390/sym12122055 - 11 Dec 2020
Cited by 2 | Viewed by 429
Abstract
In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection [...] Read more.
In this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions in terms of solvents, reagents, processes, or conditions of processes. Another important area is filling the data gaps in datasets to more fully characterize sustainable options. It is significant as many experiments are avoided, and the results are obtained with good approximation. Multivariate statistics are tools that support the application of quantitative structure–property relationships, a widely applied technique in green chemistry. Full article
(This article belongs to the Special Issue Chemometrics in Assessing Molecular Structures and Properties)
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