Chemistry

Special Issue Information

Dear Colleagues,

QSPR/QSAR analysis is a widely used tool for improving knowledge in the fields of natural sciences such as chemistry, biochemistry, medicinal chemistry, and nanochemistry, as well as chemical technology and ecology. There are followers of the above scientific fields. There are opponents in this regard. There are disagreements between researchers who are experts in this large segment of modern science. It is important to integrate different opinions to reach a consensus. It is important to integrate different conceptions of QSPR/QSAR to determine the advantages and disadvantages of various approaches. Most of the problems of QSPR/QSAR persist for a long period of time, e.g., the validation of a model and the definition of the domain of applicability. Besides the mentioned ones, new problems are realized, e.g., how to use the Internet for QSPR/QSAR applications, how to connect traditional experiments and computational experiments, and how to apply data on molecular structures to build up a predictive model. These "simple" questions have not been completely answered yet. Moreover, complete answers to the above questions have hardly even been suggested at all. The joint consideration of organic, inorganic, and metal-organic compounds is impossible. Each of the above classes of compounds requires an individual approach. Special paradigms are necessary for developing predictive QSPR/QSAR models for polymers. Factually, each kind of nanomaterial—such as a fullerene derivative, nanotube, multiwalled nanotube, or quantum dot—again requires a special approach and even a special paradigm, since most nanomaterials have no molecular structure according to "classic" interpretation at all. Nevertheless, the above tasks can be at least partially discussed in this Special Issue.

You may choose our Joint Special Issue in Molecules.

Prof. Dr. Alla P. Toropova
Guest Editor

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Published Papers (2 papers)

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Research

11 pages, 38798 KiB

Open AccessArticle

QSAR Modelling of Peptidomimetic Derivatives towards HKU4-CoV 3CL^pro Inhibitors against MERS-CoV

by Imad Hammoudan, Soumaya Matchi, Mohamed Bakhouch, Salah Belaidi and Samir Chtita

Chemistry 2021, 3(1), 391-401; https://doi.org/10.3390/chemistry3010029 - 9 Mar 2021

Cited by 10 | Viewed by 2796

Abstract

In this paper, we report the relationship between the anti-MERS-CoV activities of the HKU4 derived peptides for some peptidomimetic compounds and various descriptors using the quantitative structure activity relationships (QSAR) methods. The used descriptors were computed using ChemSketch, Marvin Sketch and ChemOffice software. The principal components analysis (PCA) and the multiple linear regression (MLR) methods were used to propose a model with reliable predictive capacity. The original data set of 41 peptidomimetic derivatives was randomly divided into training and test sets of 34 and 7 compounds, respectively. The predictive ability of the best MLR model was assessed by determination coefficient R² = 0.691, cross-validation parameter Q²_cv = 0.528 and the external validation parameter R²_test = 0.794. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications 2021)

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9 pages, 11911 KiB

Open AccessArticle

Discovery of Novel 1,2,4-Oxadiazole Derivatives as Potent Caspase-3 Activator for Cancer Treatment

by Ankur Vaidya

Chemistry 2021, 3(1), 373-381; https://doi.org/10.3390/chemistry3010027 - 8 Mar 2021

Cited by 3 | Viewed by 2327

Abstract

In the present study, a quantitative structure–activity relationship (QSAR) and docking studies were accomplished on a series of 1,2,4-oxadiazoles. The results of QSARs are reliable and have high predictive ability for both the internal (q² = 0.610) and external (pred_r² = 0.553) datasets with least standard error (SE; i.e., 0.130) and four principal components, which signifies the reliability of the generated model. Molecular docking was also reported by the GOLD docking program, which showed that the hydrogen bonding may be responsible for the activity, and may be further increased upon adding high electronegative substitutions. Full article

(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications 2021)

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