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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: closed (15 March 2021).

Special Issue Editor

Prof. Dr. Alla P. Toropova
E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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 Chemistry.

Prof. Alla P. Toropova
Guest Editor

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Keywords

  • 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

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Published Papers (16 papers)

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Research

Article
QSAR Models for Active Substances against Pseudomonas aeruginosa Using Disk-Diffusion Test Data
Molecules 2021, 26(6), 1734; https://doi.org/10.3390/molecules26061734 - 19 Mar 2021
Viewed by 1020
Abstract
Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have [...] Read more.
Pseudomonas aeruginosa is a Gram-negative bacillus included among the six “ESKAPE” microbial species with an outstanding ability to “escape” currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (support vector classifier, K nearest neighbors, random forest classifier, decision tree classifier, AdaBoost classifier, logistic regression and naïve Bayes classifier). We used four sets of molecular descriptors and fingerprints and three different methods of data balancing, together with the “native” data set. In total, 32 models were built for each set of descriptors or fingerprint and balancing method, of which 28 were selected and stacked to create meta-models. In terms of balanced accuracy, the best performance was provided by KNN, logistic regression and decision tree classifier, but the ensemble method had slightly superior results in nested cross-validation. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Predictive Capability of QSAR Models Based on the CompTox Zebrafish Embryo Assays: An Imbalanced Classification Problem
Molecules 2021, 26(6), 1617; https://doi.org/10.3390/molecules26061617 - 15 Mar 2021
Cited by 2 | Viewed by 917
Abstract
The CompTox Chemistry Dashboard (ToxCast) contains one of the largest public databases on Zebrafish (Danio rerio) developmental toxicity. The data consists of 19 toxicological endpoints on unique 1018 compounds measured in relatively low concentration ranges. The endpoints are related to developmental [...] Read more.
The CompTox Chemistry Dashboard (ToxCast) contains one of the largest public databases on Zebrafish (Danio rerio) developmental toxicity. The data consists of 19 toxicological endpoints on unique 1018 compounds measured in relatively low concentration ranges. The endpoints are related to developmental effects occurring in dechorionated zebrafish embryos for 120 hours post fertilization and monitored via gross malformations and mortality. We report the predictive capability of 209 quantitative structure–activity relationship (QSAR) models developed by machine learning methods using penalization techniques and diverse model quality metrics to cope with the imbalanced endpoints. All these QSAR models were generated to test how the imbalanced classification (toxic or non-toxic) endpoints could be predicted regardless which of three algorithms is used: logistic regression, multi-layer perceptron, or random forests. Additionally, QSAR toxicity models are developed starting from sets of classical molecular descriptors, structural fingerprints and their combinations. Only 8 out of 209 models passed the 0.20 Matthew’s correlation coefficient value defined a priori as a threshold for acceptable model quality on the test sets. The best models were obtained for endpoints mortality (MORT), ActivityScore and JAW (deformation). The low predictability of the QSAR model developed from the zebrafish embryotoxicity data in the database is mainly due to a higher sensitivity of 19 measurements of endpoints carried out on dechorionated embryos at low concentrations. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification
Molecules 2021, 26(4), 1111; https://doi.org/10.3390/molecules26041111 - 19 Feb 2021
Cited by 13 | Viewed by 2240
Abstract
Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification [...] Read more.
Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification performance itself. We compared several combinations of dataset sizes and split ratios with five different machine learning algorithms to find the differences or similarities and to select the best parameter settings in nonbinary (multiclass) classification. It is also known that the models are ranked differently according to the performance merit(s) used. Here, 25 performance parameters were calculated for each model, then factorial ANOVA was applied to compare the results. The results clearly show the differences not just between the applied machine learning algorithms but also between the dataset sizes and to a lesser extent the train/test split ratios. The XGBoost algorithm could outperform the others, even in multiclass modeling. The performance parameters reacted differently to the change of the sample set size; some of them were much more sensitive to this factor than the others. Moreover, significant differences could be detected between train/test split ratios as well, exerting a great effect on the test validation of our models. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Enhancing Carbon Acid pKa Prediction by Augmentation of Sparse Experimental Datasets with Accurate AIBL (QM) Derived Values
Molecules 2021, 26(4), 1048; https://doi.org/10.3390/molecules26041048 - 17 Feb 2021
Viewed by 756
Abstract
The prediction of the aqueous pKa of carbon acids by Quantitative Structure Property Relationship or cheminformatics-based methods is a rather arduous problem. Primarily, there are insufficient high-quality experimental data points measured in homogeneous conditions to allow for a good global model to [...] Read more.
The prediction of the aqueous pKa of carbon acids by Quantitative Structure Property Relationship or cheminformatics-based methods is a rather arduous problem. Primarily, there are insufficient high-quality experimental data points measured in homogeneous conditions to allow for a good global model to be generated. In our computationally efficient pKa prediction method, we generate an atom-type feature vector, called a distance spectrum, from the assigned ionisation atom, and learn coefficients for those atom-types that show the impact each atom-type has on the pKa of the ionisable centre. In the current work, we augment our dataset with pKa values from a series of high performing local models derived from the Ab Initio Bond Lengths method (AIBL). We find that, in distilling the knowledge available from multiple models into one general model, the prediction error for an external test set is reduced compared to that using literature experimental data alone. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
A Multi-Objective Approach for Drug Repurposing in Preeclampsia
Molecules 2021, 26(4), 777; https://doi.org/10.3390/molecules26040777 - 03 Feb 2021
Cited by 1 | Viewed by 1062
Abstract
Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets [...] Read more.
Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
In Silico Studies of Lamiaceae Diterpenes with Bioinsecticide Potential against Aphis gossypii and Drosophila melanogaster
Molecules 2021, 26(3), 766; https://doi.org/10.3390/molecules26030766 - 02 Feb 2021
Viewed by 1237
Abstract
Background: The growing demand for agricultural products has led to the misuse/overuse of insecticides; resulting in the use of higher concentrations and the need for ever more toxic products. Ecologically, bioinsecticides are considered better and safer than synthetic insecticides; they must be toxic [...] Read more.
Background: The growing demand for agricultural products has led to the misuse/overuse of insecticides; resulting in the use of higher concentrations and the need for ever more toxic products. Ecologically, bioinsecticides are considered better and safer than synthetic insecticides; they must be toxic to the target organism, yet with low or no toxicity to non-target organisms. Many plant extracts have seen their high insecticide potential confirmed under laboratory conditions, and in the search for plant compounds with bioinsecticidal activity, the Lamiaceae family has yielded satisfactory results. Objective: The aim of our study was to develop computer-assisted predictions for compounds with known insecticidal activity against Aphis gossypii and Drosophila melanogaster. Results and conclusion: Structure analysis revealed ent-kaurane, kaurene, and clerodane diterpenes as the most active, showing excellent results. We also found that the interactions formed by these compounds were more stable, or presented similar stability to the commercialized insecticides tested. Overall, we concluded that the compounds bistenuifolin L (1836) and bistenuifolin K (1931), were potentially active against A. gossypii enzymes; and salvisplendin C (1086) and salvixalapadiene (1195), are potentially active against D. melanogaster. We observed and highlight that the diterpenes bistenuifolin L (1836), bistenuifolin K (1931), salvisplendin C (1086), and salvixalapadiene (1195), present a high probability of activity and low toxicity against the species studied. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Molecular Modelling Studies on Pyrazole Derivatives for the Design of Potent Rearranged during Transfection Kinase Inhibitors
Molecules 2021, 26(3), 691; https://doi.org/10.3390/molecules26030691 - 28 Jan 2021
Cited by 3 | Viewed by 913
Abstract
RET (rearranged during transfection) kinase, one of the receptor tyrosine kinases, plays a crucial role in the development of the human nervous system. It is also involved in various cell signaling networks responsible for the normal cell division, growth, migration, and survival. Previously [...] Read more.
RET (rearranged during transfection) kinase, one of the receptor tyrosine kinases, plays a crucial role in the development of the human nervous system. It is also involved in various cell signaling networks responsible for the normal cell division, growth, migration, and survival. Previously reported clinical studies revealed that deregulation or aberrant activation of RET signaling can cause several types of human cancer. For example, medullary thyroid carcinoma (MTC) and multiple endocrine neoplasia (MEN2A, MEN2B) occur due to sporadic mutation or germline RET mutation. A number of RET kinase inhibitors have been approved by the FDA for the treatment of cancer, such as cabozantinib, vandetanib, lenvatinib, and sorafenib. However, each of these drugs is a multikinase inhibitor. Hence, RET is an important therapeutic target for cancer drug design. In this work, we have performed various molecular modelling studies, such as molecular docking and dynamics simulation for the most active compound of the pyrazole series as RET kinase inhibitors. Furthermore, molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) free energy calculation and 3-dimensional quantitative structure–activity relationship (3D-QSAR) were performed using g_mmpbsa and SYBYL-X 2.1 package. The results of this study revealed the crucial binding site residues at the active site of RET kinase and contour map analysis showed important structural characteristics for the design of new highly active inhibitors. Therefore, we have designed ten RET kinase inhibitors, which showed higher inhibitory activity than the most active compound of the series. The results of our study provide insights to design more potent and selective RET kinase inhibitors. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Identification of Tyrosinase Inhibitors and Their Structure-Activity Relationships via Evolutionary Chemical Binding Similarity and Structure-Based Methods
Molecules 2021, 26(3), 566; https://doi.org/10.3390/molecules26030566 - 22 Jan 2021
Cited by 2 | Viewed by 932
Abstract
Tyrosinase is an enzyme that plays a crucial role in the melanogenesis of humans and the browning of food products. Thus, tyrosinase inhibitors that are useful to the cosmetic and food industries are required. In this study, we have used evolutionary chemical binding [...] Read more.
Tyrosinase is an enzyme that plays a crucial role in the melanogenesis of humans and the browning of food products. Thus, tyrosinase inhibitors that are useful to the cosmetic and food industries are required. In this study, we have used evolutionary chemical binding similarity (ECBS) to screen a virtual chemical database for human tyrosinase, which resulted in seven potential tyrosinase inhibitors confirmed through the tyrosinase inhibition assay. The tyrosinase inhibition percentage for three of the new actives was over 90% compared to 61.9% of kojic acid. From the structural analysis through pharmacophore modeling and molecular docking with the human tyrosinase model, the pi–pi interaction of tyrosinase inhibitors with conserved His367 and the polar interactions with Asn364, Glu345, and Glu203 were found to be essential for tyrosinase–ligand interactions. The pharmacophore features and the docking models showed high consistency, revealing the possible essential binding interactions of inhibitors to human tyrosinase. We have also presented the activity cliff analysis that successfully revealed the chemical features related to substantial activity changes found in the new tyrosinase inhibitors. The newly identified inhibitors and their structure–activity relationships presented here will help to identify or design new human tyrosinase inhibitors. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds
Molecules 2021, 26(2), 421; https://doi.org/10.3390/molecules26020421 - 14 Jan 2021
Viewed by 833
Abstract
Using the GUSAR 2013 program, the quantitative structure–antioxidant activity relationship has been studied for 74 phenols, aminophenols, aromatic amines and uracils having lgk7 = 0.01–6.65 (where k7 is the rate constant for the reaction of antioxidants with peroxyl radicals generated upon [...] Read more.
Using the GUSAR 2013 program, the quantitative structure–antioxidant activity relationship has been studied for 74 phenols, aminophenols, aromatic amines and uracils having lgk7 = 0.01–6.65 (where k7 is the rate constant for the reaction of antioxidants with peroxyl radicals generated upon oxidation). Based on the atomic descriptors (Quantitative Neighborhood of Atoms (QNA) and Multilevel Neighborhoods of Atoms (MNA)) and molecular (topological length, topological volume and lipophilicity) descriptors, we have developed 9 statistically significant QSAR consensus models that demonstrate high accuracy in predicting the lgk7 values for the compounds of training sets and appropriately predict lgk7 for the test samples. Moderate predictive power of these models is demonstrated using metrics of two categories: (1) based on the determination coefficients R2 (R2TSi, R20, Q2(F1), Q2(F2), RmTSi2¯) and based on the concordance correlation coefficient (CCC)); or (2) based on the prediction lgk7 errors (root mean square error (RMSEP), mean absolute error (MAE) and standard deviation (S.D.)) The RBF-SCR method has been used for selecting the descriptors. Our theoretical prognosis of the lgk7 for 8-PPDA, a known antioxidant, based on the consensus models well agrees with the experimental value measure in the present work. Thus, the algorithms for calculating the descriptors implemented in the GUSAR 2013 program allow simulating kinetic parameters of the reactions underling the liquid-phase oxidation of hydrocarbons. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Classification of Congeneric and QSAR of Homologous Antileukemic S–Alkylcysteine Ketones
Molecules 2021, 26(1), 235; https://doi.org/10.3390/molecules26010235 - 05 Jan 2021
Viewed by 805
Abstract
Based on a set of six vector properties, the partial correlation diagram is calculated for a set of 28 S-alkylcysteine diazomethyl- and chloromethyl-ketone derivatives. Those with the greatest antileukemic activity in the same class correspond to high partial correlations. A periodic classification [...] Read more.
Based on a set of six vector properties, the partial correlation diagram is calculated for a set of 28 S-alkylcysteine diazomethyl- and chloromethyl-ketone derivatives. Those with the greatest antileukemic activity in the same class correspond to high partial correlations. A periodic classification is performed based on information entropy. The first four characteristics denote the group, and the last two indicate the period. Compounds in the same period and, especially, group present similar properties. The most active substances are situated at the bottom right. Nine classes are distinguished. The principal component analysis of the homologous compounds shows five subclasses included in the periodic classification. Linear fits of both antileukemic activities and stability are good. They are in agreement with the principal component analysis. The variables that appear in the models are those that show positive loading in the principal component analysis. The most important properties to explain the antileukemic activities (50% inhibitory concentration Molt-3 T-lineage acute lymphoblastic leukemia minus the logarithm of 50% inhibitory concentration Nalm-6 B-lineage acute lymphoblastic leukemia and stability k) are ACD logD, surface tension and number of violations of Lipinski’s rule of five. After leave-m-out cross-validation, the most predictive model for cysteine diazomethyl- and chloromethyl-ketone derivatives is provided. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
QSAR Model for Predicting the Cannabinoid Receptor 1 Binding Affinity and Dependence Potential of Synthetic Cannabinoids
Molecules 2020, 25(24), 6057; https://doi.org/10.3390/molecules25246057 - 21 Dec 2020
Viewed by 1025
Abstract
In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of [...] Read more.
In recent years, there have been frequent reports on the adverse effects of synthetic cannabinoid (SC) abuse. SCs cause psychoactive effects, similar to those caused by marijuana, by binding and activating cannabinoid receptor 1 (CB1R) in the central nervous system. The aim of this study was to establish a reliable quantitative structure–activity relationship (QSAR) model to correlate the structures and physicochemical properties of various SCs with their CB1R-binding affinities. We prepared tetrahydrocannabinol (THC) and 14 SCs and their derivatives (naphthoylindoles, naphthoylnaphthalenes, benzoylindoles, and cyclohexylphenols) and determined their binding affinity to CB1R, which is known as a dependence-related target. We calculated the molecular descriptors for dataset compounds using an R/CDK (R package integrated with CDK, version 3.5.0) toolkit to build QSAR regression models. These models were established, and statistical evaluations were performed using the mlr and plsr packages in R software. The most reliable QSAR model was obtained from the partial least squares regression method via Y-randomization test and external validation. This model can be applied in vivo to predict the addictive properties of illicit new SCs. Using a limited number of dataset compounds and our own experimental activity data, we built a QSAR model for SCs with good predictability. This QSAR modeling approach provides a novel strategy for establishing an efficient tool to predict the abuse potential of various SCs and to control their illicit use. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
Molecules 2020, 25(24), 5942; https://doi.org/10.3390/molecules25245942 - 15 Dec 2020
Cited by 1 | Viewed by 829
Abstract
Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and [...] Read more.
Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models’ accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Probing Substrate/Catalyst Effects Using QSPR Analysis on Friedel-Crafts Acylation Reactions over Hierarchical BEA Zeolites
Molecules 2020, 25(23), 5682; https://doi.org/10.3390/molecules25235682 - 02 Dec 2020
Cited by 1 | Viewed by 646
Abstract
Attempts to optimize heterogeneous catalysis often lack quantitative comparative analysis. The use of kinetic modelling leads to rate (k) and relative sorption equilibrium constants (K), which can be further rationalized using Quantitative Structure-Property Relationships (QSPR) based on Multiple Linear [...] Read more.
Attempts to optimize heterogeneous catalysis often lack quantitative comparative analysis. The use of kinetic modelling leads to rate (k) and relative sorption equilibrium constants (K), which can be further rationalized using Quantitative Structure-Property Relationships (QSPR) based on Multiple Linear Regressions (MLR). Friedel-Crafts acylation using commercial and hierarchical BEA zeolites as heterogeneous catalysts, acetic anhydride as the acylating agent, and a set of seven substrates with different sizes and chemical functionalities were herein studied. Catalytic results were correlated with the 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 entirely surprising, the rate and sorption 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 to the substrates’ molecular size, which can be associated with different reaction steps, namely accessibility to micropores, diffusion capacity, and polarizability of molecules. The relatively large set of substrates used here reinforces previous findings and brings further insights into the factors that hamper/speed up Friedel-Crafts reactions in heterogeneous media. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications II)
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Article
Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease
Molecules 2020, 25(21), 5172; https://doi.org/10.3390/molecules25215172 - 06 Nov 2020
Cited by 15 | Viewed by 2994
Abstract
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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Article
Discovery of Potential Chemical Probe as Inhibitors of CXCL12 Using Ligand-Based Virtual Screening and Molecular Dynamic Simulation
Molecules 2020, 25(20), 4829; https://doi.org/10.3390/molecules25204829 - 20 Oct 2020
Cited by 1 | Viewed by 858
Abstract
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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Article
Docking and QSAR of Aminothioureas at the SARS-CoV-2 S-Protein–Human ACE2 Receptor Interface
Molecules 2020, 25(20), 4645; https://doi.org/10.3390/molecules25204645 - 12 Oct 2020
Cited by 1 | Viewed by 1181
Abstract
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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