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Special Issue "QSAR and QSPR: Recent Developments and Applications II"

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 15 December 2020.

Special Issue Editor

Prof. Dr. Alla P. Toropova
Guest Editor
Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy
Interests: QSPR/QSAR; Monte Carlo method; nanoinformatics; toxicology; nanotoxicology; drug discovery
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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.

Prof. Alla P. Toropova
Guest Editor

Manuscript Submission Information

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  • Combinatorial Chemistry
  • Drug Discovery
  • Risk Assessment
  • Toxicity of Industrial Pollutants
  • Physicochemical Descriptors
  • Quantum Mechanics Descriptors
  • Optimal Descriptors
  • Topological Indices
  • Monte Carlo Method
  • Nanoinformatics
  • Molecular Docking

Related Special Issue

Published Papers (3 papers)

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Open AccessArticle
Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease
Molecules 2020, 25(21), 5172; - 06 Nov 2020
Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the [...] Read more.
Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure–Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Open AccessArticle
Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation
Molecules 2020, 25(20), 4829; - 20 Oct 2020
CXCL12 are small pro-inflammatory chemo-attractant cytokines that bind to a specific receptor CXCR4 with a role in angiogenesis, tumor progression, metastasis, and cell survival. Globally, cancer metastasis is a major cause of morbidity and mortality. In this study, we targeted CXCL12 rather than [...] Read more.
CXCL12 are small pro-inflammatory chemo-attractant cytokines that bind to a specific receptor CXCR4 with a role in angiogenesis, tumor progression, metastasis, and cell survival. Globally, cancer metastasis is a major cause of morbidity and mortality. In this study, we targeted CXCL12 rather than the chemokine receptor (CXCR4) because most of the drugs failed in clinical trials due to unmanageable toxicities. Until now, no FDA approved medication has been available against CXCL12. Therefore, we aimed to find new inhibitors for CXCL12 through virtual screening followed by molecular dynamics simulation. For virtual screening, active compounds against CXCL12 were taken as potent inhibitors and utilized in the generation of a pharmacophore model, followed by validation against different datasets. Ligand based virtual screening was performed on the ChEMBL and in-house databases, which resulted in successive elimination through the steps of pharmacophore-based and score-based screenings, and finally, sixteen compounds of various interactions with significant crucial amino acid residues were selected as virtual hits. Furthermore, the binding mode of these compounds were refined through molecular dynamic simulations. Moreover, the stability of protein complexes, Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), and radius of gyration were analyzed, which led to the identification of three potent inhibitors of CXCL12 that may be pursued in the drug discovery process against cancer metastasis. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Open AccessArticle
Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein–Human ACE2 Receptor Interface
Molecules 2020, 25(20), 4645; - 12 Oct 2020
Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein–human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure–activity relationship (QSAR) analysis. The structural variety of the studied compounds, that [...] Read more.
Docking of over 160 aminothiourea derivatives at the SARS-CoV-2 S-protein–human ACE2 receptor interface, whose structure became available recently, has been evaluated for its complex stabilizing potency and subsequently subjected to quantitative structure–activity relationship (QSAR) analysis. The structural variety of the studied compounds, that include 3 different forms of the N–N–C(S)–N skeleton and combinations of 13 different substituents alongside the extensive length of the interface, resulted in the failure of the QSAR analysis, since different molecules were binding to different parts of the interface. Subsequently, absorption, distribution, metabolism, and excretion (ADME) analysis on all studied compounds, followed by a toxicity analysis using statistical models for selected compounds, was carried out to evaluate their potential use as lead compounds for drug design. Combined, these studies highlighted two molecules among the studied compounds, i.e., 5-(pyrrol-2-yl)-2-(2-methoxyphenylamino)-1,3,4-thiadiazole and 1-(cyclopentanoyl)-4-(3-iodophenyl)-thiosemicarbazide, as the best candidates for the development of future drugs. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Toward repurposing of fluorinated drugs to combat COVID-19 outbreak through utilizing virtual screening techniques
Authors: Assem Barakat
Affiliation: King Saud University, Riyadh, Saudi Arabia
Abstract: COVID-19 pandemic is a global outbreak with of unprecedented level. We have applied different virtual screening studies for FDA approved anti- or non-antiviral fluorinated drugs to target both viral proteins and other targeting cellular proteins to investigate their binding affinity towards both SARS-CoV-2 main protease (Mpro) and spike glycoprotein. Molecular docking of fluorinated drugs with MPro (PDB ID: 6y2f) revealed that pimodivir, nilotinib, nebivolol, elagolix, raltegravir K, and pitavastatin interact powerfully with the key amino acids in the receptor clefts. While, their docking pose with spike glycoprotein (PDB ID: 6vsb) displayed that Statin drugs especially pivastatin, and fulvestrant, iloperidone, binimetinib, and nilotinib bind strongly with the spike receptor. Additionally, rapid overlay chemical structure (ROCS technique) was implemented. Pitavastatin showed high Tanimoto Combo scores and high similarity to the main protease inhibitor lopinavir drug. Sofosbuvir exhibited the top similarity to remdesivir (RNA polymerase prescribed in COVID-19 cases).

Title: Probing the substrate effect using QSPR analysis on Friedel-Crafts acylation reaction over hierarchical BEA zeolites
Authors: Ruben Elvas-Leitão1,2,*; Filomena Martins2; Leonor Borbinha2; Ângela Martins1,2; Nélson Nunes1,2,*
Affiliation: 1Área Departamental de Engenharia Química, Instituto Superior de Engenharia de Lisboa, IPL, R. Conselheiro Emídio Navarro, 1959-007 Lisboa, Portugal; 2Centro de Química Estrutural (CQE), Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal
Abstract:  Attempts to optimize heterogeneous catalysis often lack quantitative comparative analysis. The use of kinetic modelling leads to rate (k) and relative desorption equilibrium constants (K) which can be further rationalized using Quantitative Structure-Property Relationships (QSPR) based on Multiple Linear Regressions (MLR). Friedel-Crafts acylations using commercial and hierarchical BEA zeolites as heterogeneous catalysts, acetic anhydride as acylating agent and a set of seven substrates with different sizes and chemical functionalities were herein studied. Catalytic results were correlated with physicochemical properties of substrates’ and catalysts. From this analysis, a robust set of equations was obtained allowing inferences about the dominant factors governing the processes. Not totally surprisingly, rate and adsorption equilibrium constants were found to be explained in part by common factors but of opposite signs: higher and stronger adsorption forces increase reaction rates, but they also make the zeolite active sites less accessible to new reactant molecules. The most relevant parameters are related with substrates’ molecules size, which can also be associated with different reaction steps namely accessibility to micropores, diffusion capacity and polarizability of molecules. The relatively large set of substrates now used reinforces previous findings and brings further insights into the factors that hamper/speedup Friedel-Crafts reactions in heterogeneous media.

Title: 3D-QSPR study to predict the stability constant of some potentially toxic and pollutant complexes
Authors: Azize Abdolmaleki1; Fereshteh Shiri2
Affiliation: 1Department of Chemistry, Tuyserkan Branch, Islamic Azad University, Tuyserkan, Iran; 2Department of Chemistry, University of Zabol, P.O. Box 98615-538, Zabol, Iran
Abstract: The heavy metals are emerging pollutant among the most potentially toxic species to which human populations are exposed. Three-dimensional quantitative structure-property relationship (3D-QSPR) technique by generating GRid-INdependent Descriptors (GRINDs), focuses on the spatial properties of the compound. This work developed 3D-QSPR models to understanding of the effects of physicochemical properties on stability constants of heavy metals; 87 (Fe2+), 226 (Pb2+) and 261 (Mn2+) with diverse organic ligands in aqueous solutions at 298 K and an ionic strength 0.1 M. Fractional factorial design (FFD) and genetic algorithm (GA) applied to select the most relevant 3D molecular descriptors. Kennard– Stone algorithm was employed to split data set to a training set of 75% molecules and a test set of 25% molecules. The descriptors selected using various feature selection were correlated with stability constants by partial least squares (PLS). GA-PLS model gave prominent statistical values with the correlation coefficient of = 0.95, Q2= 0.83 and R2pred=0.9 for Fe2+; = 0.90, Q2= 0.72 and R2pred=0.84 for Pb2+  and = 0.87, Q2= 0.63 and R2pred=0.76 for Mn2+. Results analysis confirmed that shape and size, hydrogen bonding properties and hydrophobicity are the main parameters influencing stability constant of metal complexation.

Title: Establishment of QSAR Models for the Prediction of Abuse Potential of Synthetic Cannabinoids using R integrated with CDK
Authors: Hyun Ju Park
Affiliation: School of Pharmacy, Sungkyunkwan University, Suwon 16419, Korea
Abstract: In the recent years, the adverse effects of abuse of synthetic cannabinoids (SCs) have been frequently reported. They cause psychoactive effects similar to marijuana by binding and activating the cannabinoid receptor 1 (CB1R) in CNS. Our aim is to establish a reliable QSAR model to correlate the structures and physico-chemical properties of various SCs with their CB1R binding affinities which are related with potential addiction-related properties. A total of 15 SCs and their derivatives (THC, naphthoylindoles, and cyclohexylphenols) were prepared, and their binding affinity on CB1R, which is known as abuse-related target, were determined. To build QSAR regression models, the molecular descriptors for dataset compounds were calculated by R/CDK (R package integrated with CDK, version 3.5.0) toolkit. Generation of QSAR models and statistical evaluations were performed using the mlr (version 2.17.1), plsr (version 0.0.1) package in R program. The most reliable QSAR model was obtained from PLSR method via external validation, and this model can be applied to predict in vivo abuse propensities of new SCs. With the limited number of dataset compounds and our own experimental activity data, we built the QSAR model of SCs with a good predictability. The current QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and further scheduling of ‘drug of abuse’ candidates provisionally.

Title: Improved validation of predictive quality of classification models by the use of chance accuracy
Authors: Viktor Bojović1; Mario Lovrić1,2; Jadranko Batista3 Drago Bešlo4; Bono Lučić1,*
Affiliation: 1NMR Centre, Ruđer Bošković Institute, P.O. Box 180, Zagreb, Croatia 2Know-Center, Inffeldgasse 13, AT-8010 Graz, Austria 3Faculty of Science and Education, University of Mostar, Matice hrvatske b.b., Mostar, Bosnia and Herzegovina 4Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, Osijek, Cratia
Abstract: Classification QSAR / QSPR models are increasingly used today since many biological targets are evaluated as active or inactive. Validation procedures are best defined for QSAR models developed to be used for regulatory environmental purposes. The randomization of experimental values of the target variable (Y) has often been done in the validation of random correlation level which is present in the model (variable Y’). However, the random correlation between variables Y and Y’ can be also estimated as random agreement or random accuracy. The decrease in the accuracy of the model developed on the randomized Y variable in comparison with the original model with the actual order of values of Y variable is analysed in this work. New results and relations regarding the calculation of random accuracy between predictions obtained by the classification model (Y’) for a set of molecules and the corresponding experimental values (Y) are presented. Recently, a new accuracy parameter for binary classification models was introduced estimating the actual contribution of the model above the random accuracy. Here, its useful properties were analysed and confirmed by simulations. Furthermore, formulas for calculating the minimal and the maximal accuracy values as well as the average random accuracy are derived and verified by simulations. These three values are named as the characteristic values of the accuracy parameter. Generalization of their application to continuous data is also analysed. The usefulness of these novel parameters (and their differences) is illustrated in the comparison of accuracy/quality of models developed by several research groups on large sets of chemicals in the field of environmental toxicity modelling. It is recommended to calculate and use these novel parameters in optimisation and selection of classification models, as well as in ranking classification models based on their real contribution over the level of random accuracy, rather than commonly used model quality metrics.

Title: Structure-activity relationship of zinc chelating tripeptides: the novel peptide databases and the QSAR model
Authors: Ningning Xie
Affiliation: Anhui Academy of Agricultural Sciences, Hefei, China
Abstract: A new quantitative structure activity relationship (QSAR) model is established for tripeptides that exert zinc chelating activity. The zinc chelating peptide from rapeseed protein digests, Ala-Ser-His (ASH), was identified by electrospray ionization–tandem mass spectrometry and theoretical calculation. The novel peptide database containing 61 tripeptides based on ASH and the corresponding detected activities was established. It was then statistically analyzed using 18 kinds of amino acid descriptors with two-dimensional or three-dimensional properties. Models were estimated using partial least squares regression and validated through full cross-validation and external validation (R2>0.7, Q2>0.5). Results showed that the five two-dimensional descriptors, including Z-scale descriptor(Z), vectors of hydrophobic, steric, and electronic property (VHSE), score vectors of the zero-dimension, one dimension, two dimensions and three dimensions (SZOTT) descriptor, factor analysis scale of generalized amino acid information (FASGAI) descriptor and hydrophobic, electronic, steric, and hydrogen (HESH), exerted good performances. Especially, FASGAI descriptor showed excellent correlation coefficients with R2=0.8580, Q2=0.6479, RMSEE=0.1386, Q2ext=0.8420, RMSEP=0.2183. The results founded a relationship between the physical chemical properties of the C-terminal and N-terminal regions and zinc chelating potency. The properties of amino acids at N-terminal regions (N1>N2) were more important than those at the C-terminal regions (C1) for predicting zinc chelating activity for tripeptides. These results contribute to the theory of zinc supplementations in food matrix and its application in medicine.

Title: QSAR Study of the Spacer Group Effect on Antibacterial Activity of Cationic Gemini Surfactants Against Escherichia Coli Using Molecular Connectivity Indices
Authors: Anna Mozrzymas
Affiliation: Wroclaw University of Environmental and Life Sciences, Wroclaw, Poland
Abstract: Molecular connectivity indices were used to study the effect of the spacer group structure on the value of minimum inhibitory concentration of cationic (bromide) gemini surfactants. The effects of branching, multiple bonds and heteroatoms in the spacer group have been studied theoretically. QSAR model obtained leads to some significant conclusions about the effect of the spacer group structure on minimum inhibitory concentration. The general conclusion is that a small structure modification of the straight spacer chain can give gemini surfactant with excellent activity against such resistant bacterium as Escherichia coli

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