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

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

Deadline for manuscript submissions: closed (31 January 2019).

Special Issue Editor

Prof. Kok Hwa Lim
Website
Guest Editor
Singapore Institute of Technology, Singapore City, Singapore
Interests: computational chemistry and material sciences; heterogeneous catalytic reactions and surface sciences; green chemistry and processes; process safety; QSAR analysis of biological activity

Special Issue Information

Dear Colleagues,

QSAR modeling is an integral part of rational drug design (RDD). Despite the prediction of biological activities, QSAR models help to identify the parameters responsible for biological response that is essential for lead compound optimization. In addition, recent developments in molecular docking have been successful to provide information such relative orientation of drug molecules binding to their targeted receptor leading to optimization of lead compound to achieve more potent and selective analogs. Despite the successful application of QSAR to predict biological activities, few QSAR studies have been reported on biological activities of metal-complexes, probably due to the lack of specific metal ligand parameters. Recently, the successful use of density functional theory (DFT) to calculate chemical descriptors of metal complexes also open-up new era for QSAR studies on metal complexes. This Special Issue of Molecules will consider submissions related to QSAR of biological activities. For examples, prediction of biological activities of metal-complexes or molecular entities using physicochemical, steric, topological, as well as ab-initio quantum chemical, pharmacophore mapping and molecular docking descriptors.

Prof. Kok Hwa Lim
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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

  • Ab-initio
  • semi-empirical quantum chemical methods
  • topological
  • physicochemical
  • electronic descriptors
  • metal complexes
  • pharmacophore mapping
  • molecular docking
  • lead compound optimization

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Published Papers (16 papers)

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Research

Open AccessArticle
Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search
Molecules 2019, 24(12), 2233; https://doi.org/10.3390/molecules24122233 - 14 Jun 2019
Cited by 5
Abstract
In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality [...] Read more.
In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Developing Predictive Models for Carrying Ability of Micro-Plastics towards Organic Pollutants
Molecules 2019, 24(9), 1784; https://doi.org/10.3390/molecules24091784 - 08 May 2019
Abstract
Microplastics, which have been frequently detected worldwide, are strong adsorbents for organic pollutants and may alter their environmental behavior and toxicity in the environment. To completely state the risk of microplastics and their coexisting organics, the adsorption behavior of microplastics is a critical [...] Read more.
Microplastics, which have been frequently detected worldwide, are strong adsorbents for organic pollutants and may alter their environmental behavior and toxicity in the environment. To completely state the risk of microplastics and their coexisting organics, the adsorption behavior of microplastics is a critical issue that needs to be clarified. Thus, the microplastic/water partition coefficient (log Kd) of organics was investigated by in silico method here. Five log Kd predictive models were developed for the partition of organics in polyethylene/seawater, polyethylene/freshwater, polyethylene/pure water, polypropylene/seawater, and polystyrene/seawater. The statistical results indicate that the established models have good robustness and predictive ability. Analyzing the descriptors selected by different models finds that hydrophobic interaction is the main adsorption mechanism, and π−π interaction also plays a crucial role for the microplastics containing benzene rings. Hydrogen bond basicity and cavity formation energy of compounds can determine their partition tendency. The distinct crystallinity and aromaticity make different microplastics exhibit disparate adsorption carrying ability. Environmental medium with high salinity can enhance the adsorption of organics and microplastics by increasing their induced dipole effect. The models developed in this study can not only be used to estimate the log Kd values, but also provide some necessary mechanism information for the further risk studies of microplastics. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Computer-Aided Discovery of Small Molecules Targeting the RNA Splicing Activity of hnRNP A1 in Castration-Resistant Prostate Cancer
Molecules 2019, 24(4), 763; https://doi.org/10.3390/molecules24040763 - 20 Feb 2019
Cited by 1
Abstract
The heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) is a versatile RNA-binding protein playing a critical role in alternative pre-mRNA splicing regulation in cancer. Emerging data have implicated hnRNP A1 as a central player in a splicing regulatory circuit involving its direct transcriptional control [...] Read more.
The heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) is a versatile RNA-binding protein playing a critical role in alternative pre-mRNA splicing regulation in cancer. Emerging data have implicated hnRNP A1 as a central player in a splicing regulatory circuit involving its direct transcriptional control by c-Myc oncoprotein and the production of the constitutively active ligand-independent alternative splice variant of androgen receptor, AR-V7, which promotes castration-resistant prostate cancer (CRPC). As there is an urgent need for effective CRPC drugs, targeting hnRNP A1 could, therefore, serve a dual purpose of preventing AR-V7 generation as well as reducing c-Myc transcriptional output. Herein, we report compound VPC-80051 as the first small molecule inhibitor of hnRNP A1 splicing activity discovered to date by using a computer-aided drug discovery approach. The inhibitor was developed to target the RNA-binding domain (RBD) of hnRNP A1. Further experimental evaluation demonstrated that VPC-80051 interacts directly with hnRNP A1 RBD and reduces AR-V7 messenger levels in 22Rv1 CRPC cell line. This study lays the groundwork for future structure-based development of more potent and selective small molecule inhibitors of hnRNP A1–RNA interactions aimed at altering the production of cancer-specific alternative splice isoforms. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure—Property Relationship Approach
Molecules 2019, 24(4), 748; https://doi.org/10.3390/molecules24040748 - 19 Feb 2019
Cited by 3
Abstract
The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure−property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular [...] Read more.
The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure−property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular structures. Twelve different mixing rules were employed to derive mixture descriptors for describing the structures characteristics of a series of 181 binary hydrocarbon mixtures. Genetic algorithm (GA)-based multiple linear regression (MLR) was used to select the most statistically effective mixture descriptors on the LFL of binary hydrocarbon gases. A total of 12 multilinear models were obtained based on the different mathematical formulas. The best model, issued from the norm of the molar contribution formula, was achieved as a six-parameter model. The best model was then rigorously validated using multiple strategies and further extensively compared to the previously published model. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The applicability domain (AD) of the model was defined as well. The proposed best model would be expected to present an alternative to predict the LFL values of existing or new binary hydrocarbon gases, and provide some guidance for prioritizing the design of safer blended gases with desired properties. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
DesMol2, an Effective Tool for the Construction of Molecular Libraries and Its Application to QSAR Using Molecular Topology
Molecules 2019, 24(4), 736; https://doi.org/10.3390/molecules24040736 - 18 Feb 2019
Cited by 1
Abstract
A web application, DesMol2, which offers two main functionalities, is presented: the construction of molecular libraries and the calculation of topological indices. These functionalities are explained through a practical example of research of active molecules to the formylpeptide receptor (FPR), a receptor associated [...] Read more.
A web application, DesMol2, which offers two main functionalities, is presented: the construction of molecular libraries and the calculation of topological indices. These functionalities are explained through a practical example of research of active molecules to the formylpeptide receptor (FPR), a receptor associated with chronic inflammation in systemic amyloidosis and Alzheimer’s disease. Starting from a data(base) of 106 dioxopiperazine pyrrolidin piperazine derivatives and their respective constant values of binding affinity to FPR, multilinear regression and discriminant analyses are performed to calculate several predictive topological-mathematical models. Next, using the DesMol2 application, a molecular library consisting of 6,120 molecules is built and performed for each predictive model. The best potential active candidates are selected and compared with results from other previous works. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Combining a QSAR Approach and Structural Analysis to Derive an SAR Map of Lyn Kinase Inhibition
Molecules 2018, 23(12), 3271; https://doi.org/10.3390/molecules23123271 - 11 Dec 2018
Cited by 1
Abstract
Lyn kinase, a member of the Src family of protein tyrosine kinases, is mainly expressed by various hematopoietic cells, neural and adipose tissues. Abnormal Lyn kinase regulation causes various diseases such as cancers. Thus, Lyn represents, a potential target to develop new antitumor [...] Read more.
Lyn kinase, a member of the Src family of protein tyrosine kinases, is mainly expressed by various hematopoietic cells, neural and adipose tissues. Abnormal Lyn kinase regulation causes various diseases such as cancers. Thus, Lyn represents, a potential target to develop new antitumor drugs. In the present study, using 176 molecules (123 training set molecules and 53 test set molecules) known by their inhibitory activities (IC50) against Lyn kinase, we constructed predictive models by linking their physico-chemical parameters (descriptors) to their biological activity. The models were derived using two different methods: the generalized linear model (GLM) and the artificial neural network (ANN). The ANN Model provided the best prediction precisions with a Square Correlation coefficient R2 = 0.92 and a Root of the Mean Square Error RMSE = 0.29. It was able to extrapolate to the test set successfully (R2 = 0.91 and RMSE = 0.33). In a second step, we have analyzed the used descriptors within the models as well as the structural features of the molecules in the training set. This analysis resulted in a transparent and informative SAR map that can be very useful for medicinal chemists to design new Lyn kinase inhibitors. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Screening, Synthesis, and QSAR Research on Cinnamaldehyde-Amino Acid Schiff Base Compounds as Antibacterial Agents
Molecules 2018, 23(11), 3027; https://doi.org/10.3390/molecules23113027 - 20 Nov 2018
Cited by 3
Abstract
Development of new drugs is one of the solutions to fight against the existing antimicrobial resistance threat. Cinnamaldehyde-amino acid Schiff base compounds, are newly discovered compounds that exhibit good antibacterial activity against gram-positive and gram-negative bacteria. Quantitative structure–activity relationship (QSAR) methodology was applied [...] Read more.
Development of new drugs is one of the solutions to fight against the existing antimicrobial resistance threat. Cinnamaldehyde-amino acid Schiff base compounds, are newly discovered compounds that exhibit good antibacterial activity against gram-positive and gram-negative bacteria. Quantitative structure–activity relationship (QSAR) methodology was applied to explore the correlation between antibacterial activity and compound structures. The two best QSAR models showed R2 = 0.9354, F = 57.96, and s2 = 0.0020 against Escherichia coli, and R2 = 0.8946, F = 33.94, and s2 = 0.0043 against Staphylococcus aureus. The model analysis showed that the antibacterial activity of cinnamaldehyde compounds was significantly affected by the polarity parameter/square distance and the minimum atomic state energy for an H atom. According to the best QSAR model, the screening, synthesis, and antibacterial activity of three cinnamaldehyde-amino acid Schiff compounds were reported. The experiment value of antibacterial activity demonstrated that the new compounds possessed excellent antibacterial activity that was comparable to that of ciprofloxacin. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
QSAR and Molecular Docking Studies of the Inhibitory Activity of Novel Heterocyclic GABA Analogues over GABA-AT
Molecules 2018, 23(11), 2984; https://doi.org/10.3390/molecules23112984 - 15 Nov 2018
Cited by 1
Abstract
We have previously reported the synthesis, in vitro and in silico activities of new GABA analogues as inhibitors of the GABA-AT enzyme from Pseudomonas fluorescens, where the nitrogen atom at the γ-position is embedded in heterocyclic scaffolds. With the goal of finding [...] Read more.
We have previously reported the synthesis, in vitro and in silico activities of new GABA analogues as inhibitors of the GABA-AT enzyme from Pseudomonas fluorescens, where the nitrogen atom at the γ-position is embedded in heterocyclic scaffolds. With the goal of finding more potent inhibitors, we now report the synthesis of a new set of GABA analogues with a broader variation of heterocyclic scaffolds at the γ-position such as thiazolidines, methyl-substituted piperidines, morpholine and thiomorpholine and determined their inhibitory potential over the GABA-AT enzyme from Pseudomonas fluorescens. These structural modifications led to compound 9b which showed a 73% inhibition against this enzyme. In vivo studies with PTZ-induced seizures on male CD1 mice show that compound 9b has a neuroprotective effect at a 0.50 mmole/kg dose. A QSAR study was carried out to find the molecular descriptors associated with the structural changes in the GABA scaffold to explain their inhibitory activity against GABA-AT. Employing 3D molecular descriptors allowed us to propose the GABA analogues enantiomeric active form. To evaluate the interaction with Pseudomonas fluorescens and human GABA-AT by molecular docking, the constructions of homology models was carried out. From these calculations, 9b showed a strong interaction with both GABA-AT enzymes in agreement with experimental results and the QSAR model, which indicates that bulky ligands tend to be the better inhibitors especially those with a sulfur atom on their structure. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Computer-Aided Discovery of Small Molecule Inhibitors of Transcriptional Activity of TLX (NR2E1) Nuclear Receptor
Molecules 2018, 23(11), 2967; https://doi.org/10.3390/molecules23112967 - 14 Nov 2018
Abstract
Orphan nuclear receptor TLX (NR2E1) plays a critical role in the regulation of neural stem cells (NSC) as well as in the development of NSC-derived brain tumors. In the last years, new data have emerged implicating TLX in prostate and breast cancer. Therefore, [...] Read more.
Orphan nuclear receptor TLX (NR2E1) plays a critical role in the regulation of neural stem cells (NSC) as well as in the development of NSC-derived brain tumors. In the last years, new data have emerged implicating TLX in prostate and breast cancer. Therefore, inhibitors of TLX transcriptional activity may have a significant impact on the treatment of several critical malignancies. However, the TLX protein possesses a non-canonical ligand-binding domain (LBD), which lacks a ligand-binding pocket (conventionally targeted in case of nuclear receptors) that complicates the development of small molecule inhibitors of TLX. Herein, we utilized a rational structure-based design approach to identify small molecules targeting the Atro-box binding site of human TLX LBD. As a result of virtual screening of ~7 million molecular structures, 97 compounds were identified and evaluated in the TLX-responsive luciferase reporter assay. Among those, three chemicals demonstrated 40–50% inhibition of luciferase-detected transcriptional activity of the TLX orphan nuclear receptor at a dose of 35 µM. The identified compounds represent the first class of small molecule inhibitors of TLX transcriptional activity identified via methods of computer-aided drug discovery. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
The Internal Relation between Quantum Chemical Descriptors and Empirical Constants of Polychlorinated Compounds
Molecules 2018, 23(11), 2935; https://doi.org/10.3390/molecules23112935 - 10 Nov 2018
Abstract
Quantum chemical descriptors and empirical parameters are two different types of chemical parameters that play the fundamental roles in chemical reactivity and model development. However, previous studies have lacked detail regarding the relationship between quantum chemical descriptors and empirical constants. We selected polychlorinated [...] Read more.
Quantum chemical descriptors and empirical parameters are two different types of chemical parameters that play the fundamental roles in chemical reactivity and model development. However, previous studies have lacked detail regarding the relationship between quantum chemical descriptors and empirical constants. We selected polychlorinated biphenyls (PCBs) as an object to investigate the intrinsic correlation between 16 quantum chemical descriptors and Hammett constants. The results exhibited extremely high linearity for σ o ,   m ,   p + with Qxx/yy/zz, α and EHOMO based on the meta-position grouping. Polychlorinated dibenzodioxins (PCDDs) and polychlorinated naphthalenes (PCNs) congeners, as two independent compounds, validated the reliability of the relationship. The meta-substituent grouping method between σ o ,   m ,   p + and α was successfully used to predict the rate constant (k) for OH oxidation of PCBs, as well as the octanol/water partition coefficient (logKOW) and aqueous solubility (−logSW) of PCDDs, and exhibited excellent agreement with experimental measurements. Revealing the intrinsic correlation underlying the empirical constant and quantum chemical descriptors can develop simpler and higher efficient model application in predicting the environmental behavior and chemical properties of compounds. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Molecular Modeling and Design Studies of Purine Derivatives as Novel CDK2 Inhibitors
Molecules 2018, 23(11), 2924; https://doi.org/10.3390/molecules23112924 - 09 Nov 2018
Cited by 4
Abstract
Cyclin-dependent kinase 2 (CDK2) is a potential target for treating cancer. Purine heterocycles have attracted particular attention as the scaffolds for the development of CDK2 inhibitors. To explore the interaction mechanism and the structure–activity relationship (SAR) and to design novel candidate compounds as [...] Read more.
Cyclin-dependent kinase 2 (CDK2) is a potential target for treating cancer. Purine heterocycles have attracted particular attention as the scaffolds for the development of CDK2 inhibitors. To explore the interaction mechanism and the structure–activity relationship (SAR) and to design novel candidate compounds as potential CDK2 inhibitors, a systematic molecular modeling study was conducted on 35 purine derivatives as CDK2 inhibitors by combining three-dimensional quantitative SAR (3D-QSAR), virtual screening, molecular docking, and molecular dynamics (MD) simulations. The predictive CoMFA model (q2 = 0.743, r pred 2 = 0.991), the CoMSIA model (q2 = 0.808, r pred 2 = 0.990), and the Topomer CoMFA model (q2 = 0.779, r pred 2 = 0.962) were obtained. Contour maps revealed that the electrostatic, hydrophobic, hydrogen bond donor and steric fields played key roles in the QSAR models. Thirty-one novel candidate compounds with suitable predicted activity (predicted pIC50 > 8) were designed by using the results of virtual screening. Molecular docking indicated that residues Asp86, Glu81, Leu83, Lys89, Lys33, and Gln131 formed hydrogen bonds with the ligand, which affected activity of the ligand. Based on the QSAR model prediction and molecular docking, two candidate compounds, I13 and I60 (predicted pIC50 > 8, docking score > 10), with the most potential research value were further screened out. MD simulations of the corresponding complexes of these two candidate compounds further verified their stability. This study provided valuable information for the development of new potential CDK2 inhibitors. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes
Molecules 2018, 23(11), 2756; https://doi.org/10.3390/molecules23112756 - 24 Oct 2018
Cited by 4
Abstract
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, [...] Read more.
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Anti-Hyperuricemic Effect of 2-Hydroxy-4-methoxy-benzophenone-5-sulfonic Acid in Hyperuricemic Mice through XOD
Molecules 2018, 23(10), 2671; https://doi.org/10.3390/molecules23102671 - 17 Oct 2018
Cited by 1
Abstract
Conventionally, benzophenone-type molecules are beneficial for alleviating the UV exposure of humans. More importantly, various compounds with this skeleton have demonstrated various biological activities. In this paper, we report the anti-hyperuricemic effect of the benzophenone compound 2-hydroxy-4-methoxybenzophenone-5-sulfonic acid (HMS). Preliminarily, its molecular docking [...] Read more.
Conventionally, benzophenone-type molecules are beneficial for alleviating the UV exposure of humans. More importantly, various compounds with this skeleton have demonstrated various biological activities. In this paper, we report the anti-hyperuricemic effect of the benzophenone compound 2-hydroxy-4-methoxybenzophenone-5-sulfonic acid (HMS). Preliminarily, its molecular docking score and xanthine oxidase (XOD) inhibition suggested a good anti-hyperuricemic effect. Then, its anti-hyperuricemic effect, primary mechanisms and general toxicity were examined on a hyperuricemic mouse model which was established using potassium oxonate and hypoxanthine together. HMS demonstrated a remarkable anti- hyperuricemic effect which was near to that of the control drugs, showing promising perspective. General toxicity was assessed and it showed no negative effects on body weight growth and kidney function. Moreover, anti-inflammatory action was observed for HMS via spleen and thymus changes. Its anti-hyperuricemic mechanisms may be ascribed to its inhibition of XOD and its up-regulation of organic anion transporter 1 (OAT1) and down-regulation of glucose transporter 9 (GLUT9). Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
QSAR Study of N-Myristoyltransferase Inhibitors of Antimalarial Agents
Molecules 2018, 23(9), 2348; https://doi.org/10.3390/molecules23092348 - 13 Sep 2018
Cited by 1
Abstract
Malaria is a disease caused by protozoan parasites of the genus Plasmodium that affects millions of people worldwide. In recent years there have been parasite resistances to several drugs, including the first-line antimalarial treatment. With the aim of proposing new drugs candidates for [...] Read more.
Malaria is a disease caused by protozoan parasites of the genus Plasmodium that affects millions of people worldwide. In recent years there have been parasite resistances to several drugs, including the first-line antimalarial treatment. With the aim of proposing new drugs candidates for the treatment of disease, Quantitative Structure–Activity Relationship (QSAR) methodology was applied to 83 N-myristoyltransferase inhibitors, synthesized by Leatherbarrow et al. The QSAR models were developed using 63 compounds, the training set, and externally validated using 20 compounds, the test set. Ten different alignments for the two test sets were tested and the models were generated by the technique that combines genetic algorithms and partial least squares. The best model shows r2 = 0.757, q2adjusted = 0.634, R2pred = 0.746, R2m = 0.716, ∆R2m = 0.133, R2p = 0.609, and R2r = 0.110. This work suggested a good correlation with the experimental results and allows the design of new potent N-myristoyltransferase inhibitors. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Novel Group of AChE Reactivators—Synthesis, In Vitro Reactivation and Molecular Docking Study
Molecules 2018, 23(9), 2291; https://doi.org/10.3390/molecules23092291 - 07 Sep 2018
Cited by 4
Abstract
The acetylcholinesterase (AChE) reactivators (e.g., obidoxime, asoxime) became an essential part of organophosphorus (OP) poisoning treatment, together with atropine and diazepam. They are referred to as a causal treatment of OP poisoning, because they are able to split the OP moiety from AChE [...] Read more.
The acetylcholinesterase (AChE) reactivators (e.g., obidoxime, asoxime) became an essential part of organophosphorus (OP) poisoning treatment, together with atropine and diazepam. They are referred to as a causal treatment of OP poisoning, because they are able to split the OP moiety from AChE active site and thus renew its function. In this approach, fifteen novel AChE reactivators were determined. Their molecular design originated from former K-oxime compounds K048 and K074 with remaining oxime part of the molecule and modified part with heteroarenium moiety. The novel compounds were prepared, evaluated in vitro on human AChE (HssAChE) inhibited by tabun, paraoxon, methylparaoxon or DFP and compared to commercial HssAChE reactivators (pralidoxime, methoxime, trimedoxime, obidoxime, asoxime) or previously prepared compounds (K048, K074, K075, K203). Some of presented oxime reactivators showed promising ability to reactivate HssAChE comparable or higher than the used standards. The molecular modelling study was performed with one compound that presented the ability to reactivate GA-inhibited HssAChE. The SAR features concerning the heteroarenium part of the reactivator’s molecule are described. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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Open AccessArticle
Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis
Molecules 2018, 23(9), 2183; https://doi.org/10.3390/molecules23092183 - 30 Aug 2018
Cited by 11
Abstract
Two 3D quantitative structure–activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB1 and CB2) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB1 and CB2 ligands. [...] Read more.
Two 3D quantitative structure–activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB1 and CB2) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB1 and CB2 ligands. A set of 312 molecules have been used to build the model for the CB1 receptor, and a set of 187 molecules for the CB2 receptor. All of the molecules were recovered from the literature among those possessing measured Ki values, and Forge was used as software. The present model shows high and robust predictive potential, confirmed by the quality of the statistical analysis, and an adequate descriptive capability. A visual understanding of the hydrophobic, electrostatic, and shaping features highlighting the principal interactions for the CB1 and CB2 ligands was achieved with the construction of 3D maps. The predictive capabilities of the model were then used for a scaffold-hopping study of two selected compounds, with the generation of a library of new compounds with high affinity for the two receptors. Herein, we report two new 3D-QSAR models that comprehend a large number of chemically different CB1 and CB2 ligands and well account for the individual ligand affinities. These features will facilitate the recognition of new potent and selective molecules for CB1 and CB2 receptors. Full article
(This article belongs to the Special Issue QSAR and QSPR: Recent Developments and Applications)
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