Quantitative Structure-activity Relationship Studies Research Articles

Identification of potential Staphylococcus aureus dihydrofolate reductase inhibitors using QSAR, molecular docking, dynamics simulations and free energy calculation

Herein we describe the use of molecular docking simulations, quantitative structure-activity relationships studies and ADMETox predictions to analyse the molecular recognition of a series of 7-aryl-2,4-diaminoquinazoline derivatives on the inhibition of Staphylococcus aureus dihydrofolate reductase and conducted a virtual screening to discover new potential inhibitors. A quantitative structure-activity relationship model was developed using 40 compounds and two selected descriptors. These descriptors indicated the importance of pKa and molar refractivity for the inhibitory activity against SaDHFR. The values of R2 train, CVLOO and R2 test generated by the model were 0.808, 0.766, and 0.785, respectively. The integration between QSAR, molecular docking, ADMETox analysis and molecular dynamics simulations with binding free energies calculation, yielded the compounds PC-124127620, PC-124127795 and PC-124127805 as promising candidates to SaDHFR inhibitors. These compounds presented high potency, good pharmacokinetics and toxicological profile. Thus, these molecules are good potential antimicrobial agent to treatment of infect disease caused by S. aureus. Communicated by Ramaswamy H. Sarma

QSAR study of antituberculosis activity of oxadiazole derivatives using DFT calculations

Mycobacterium tuberculosis (Mtb) is the causative agent of infectious diseases worldwide. Oxadiazole derivatives have many biological activities and can be a good alternative to antimicrobial drugs. In this study, the quantitative structure–activity relationship (QSAR) of fifty-one novel oxadiazoles derivatives has been studied using the density functional theory (DFT) and statistical methods. Becke’s three-parameter hybrid method and the Lee-Yang-Parr B3LYP functional employing 6–31++G (d) basis set are used to calculated quantum chemical descriptors using Gaussian09 software. The other descriptors including Lipinski, physicochemistry, topological, etc. were calculated using Chembio3d software. Statistically, the best correlation between the independent variables and the PMIC as the dependent variable was a 6-variable equation for which the correlation coefficient were as follows R 2 = 0.86 and R = 0.93. Also, the values of MAE = 0.003 and Q 2 CV = 0.9 confirm the acceptability of the obtained model. The obtained equation shows that NRB, energy gap (ΔE), Henry’s law constant, O–C, and C–N bonds length, and the Free Gibbs energy have the highest correlation with the anti-Tb activity.

Quantitative Structure-activity Relationship Studies and Nonlinear Optical Properties of 2-Phenylbenzofuran Derivatives: A Density Functional Theory Study

Density Functional Theory calculations, in the ground state of 2-Phenylbenzofuran, were carried out using the GGA-PBE, PBV86 and meta-GGA-TPSS hybrid functionals with 6-31G (d, p) as a basis set. First, theoretical calculations were performed using these functionals to obtain the stable conformer of the molecule. In addition, Mulliken population natural population and natural bond orbital analyses were calculated. The molecular electrostatic potential, band gap energies, global, local chemical reactivity descriptors and non-linear optical (NLO) properties were studied. Additionally, the NLO properties of 2-Phenylbenzofuran and those of its derivatives were investigated by GGA-PBE/6-31G (d,p) level of theory. The first-order hyperpolarizability value of all 2-Phenylbenzofuran derivatives was found within the range extending from 4.00 × 10-30 to 43.57 × 10-30 (esu). It indicated that they possess remarkable NLO properties. In addition, a multiple linear regression procedure was used to envisage the relationships between molecular descriptors and the activity of 2-Phenylbenzofuran derivatives; the quantitative structure-activity relationship (QSAR) studies were performed on them using quantum descriptors. The QSAR was applied to determine a correlation between the various physico-chemical parameters of the studied compounds and their biological activities. The statistical quality of the QSAR models was assessed using statistical parameters, i.e. R2, R2adj and R2cv.

Outliers in SAR and QSAR: 4. effects of allosteric protein-ligand interactions on the classical quantitative structure-activity relationships.

Effects of allosteric interactions on the classical structure-activity relationship (SAR) and quantitative SAR (QSAR) have been investigated. Apprehending the outliers in SAR and QSAR studies can improve the quality, predictability, and use of QSAR in designing unknown compounds in drug discovery research. We explored allosteric protein-ligand interactions as a possible source of outliers in SAR/QSAR. We used glycogen phosphorylase as an example of a protein that has an allosteric site. Examination of the ligand-bound x-ray crystal structures of glycogen phosphorylase revealed that many inhibitors bound at more than one binding site. The results of QSAR analyses of the inhibitors included a QSAR that recognized an outlier bound at a distinctive allosteric binding site. The case provided an example of constructive use of QSAR identifying outliers with alternative binding modes. Other allosteric QSARs that captured our attention were the inverted parabola/bilinear QSARs. The x-ray crystal structures and the QSAR analyses indicated that the inverted parabola QSARs could be associated with the conformational changes in the allosteric interactions. Our results showed that the normal parabola, as well as the inverted parabola QSARs, can describe the allosteric interactions. Examination of the ligand-bound X-ray crystal structures of glycogen phosphorylase revealed that many inhibitors bound at more than one binding site. The results of QSAR analyses of the inhibitors included a QSAR that recognized an outlier bound at a distinctive allosteric binding site.

Application of the LAD-LASSO as a dimensional reduction technique in the ANN-based QSAR study: Discovery of potent inhibitors using molecular docking simulation

In this study, the combination of the least absolute deviation-least absolute shrinkage and selection operator (LAD-LASSO) was introduced as a new variable selection method for the artificial neural network (ANN)-based quantitative structure-activity relationship (QSAR) studies. The biological activity of various chemical compounds was predicted using an ANN-based QSAR model combined with the efficient LAD-LASSO variable selection method. In this study, 3224 computed DRAGON descriptors were reduced to a smaller number using preprocessing methods. The descriptors with the most significant relevance to biological activities were chosen using the LAD-LASSO variable selection method. The selected descriptors were defined as ANN inputs and optimized the designed models. The biological activity of the test set compounds was predicted using the optimum ANN models. The coefficients of determination (R2) for the test data in the different datasets were equal to 0.87, 0.84, and 0.87. Also, the MSE value of the test set is equal to 0.13, 0.07, and 0.11, respectively. The high R2 and low MSE values demonstrate the good prediction ability of the constructed QSAR models. The applicability domain (AD) and Y-randomization test also proved the efficiency of the developed models. Finally, The performance of the QSAR model was evaluated by the identification of novel compounds with high potency. As a result, the weak structure of the dataset was identified and modified using the effect of selected descriptors on the biological activity, resulting in the establishment of new compounds with significant potency. The response value of the new suggested compounds was predicted using the optimum ANN models. Receptor-ligand interactions were extracted for all proposed compounds. The presence of different hydrophilic and hydrophobic interactions in the active site of the respective receptor indicates the high potential of suggested chemical compounds.

Quantitative structure-activity relationship study on the degradation of polyhalogenated carbazoles by sulfidated zero-valent iron/peroxymonosulfate system

Polyhalogenated carbazoles (PHCs) are an emerging class of halogenated organic contaminants, which have widespread occurrence and dioxin-like toxicities. However, effective approaches for PHCs degradation are lacking. Recently, persulfate based advanced oxidation processes (AOPs) are considered as promising technologies for aqueous organic contaminants destruction due to the high reactivity and selectivity of sulfate radical. In this study, the degradation kinetics and mechanism of PHCs by sulfidated zero-valent iron/peroxymonosulfate (S-ZVI/PMS) system were investigated. The pseudo-first-order rate constants (k) of 11 PHCs ranged from 0.051 to 1.623 min−1. Seventeen quantum chemical descriptors were calculated based on density functional theory (DFT). By using correlation analysis, principal component analysis (PCA) and multiple linear regression (MLR), a quantitative structure activity relationship (QSAR) model was developed: log k = 8.681 ×f(0)n + 0.231 × #X – 0.593, where f(0)n and #X represent the minimum value of Fukui index for radical attack and the halogen number, respectively. The correlation analysis and model interpretation indicate that radical addition is the dominant reaction pathway for PHCs instead of single electron transfer (SET). Results of validation and applicability domain indicate that the developed QSAR model has good robustness and satisfactory predictive performance. Finally, the degradation kinetics of five frequently detected but commercially unavailable PHCs were predicted. The overall purpose of this study is to develop a predictive model and interpret the degradation mechanism of PHCs in PMS-AOP, which is difficult to be distinguished by experimental results alone.

In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study.

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic β-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure–activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms. The best model (M2) meets the Tropsha’s test requirements and has the statistics parameters R2 = 0.843, Q2CV = 0.785, and Q2ext = 0.855. Also, coverage of 100% of the test set based on the applicability domain analysis was obtained. Furthermore, a deep analysis based on the ADME predictions, molecular docking, and molecular dynamics simulations was performed. The lipophilicity and the residue interactions were used as relevant criteria for selecting a candidate from the screening of the DiaNat and DrugBank databases. Finally, the FDA-approved drugs bilastine, bromfenac, and fenofibric acid are suggested as potential and lead FFA1 agonists.

3D QSAR and pharmacophore studies on inhibitors of insuline like growth factor 1 receptor (IGF-1R) and insulin receptor (IR) as potential anti-cancer agents

Insulin like growth factor receptor (IGF-1R) and Insulin receptor (IR) are widely accepted to play a prominent role in cancer drug discovery due to their well-established involvement in various stages of tumorigenesis. Previously, neutralization of IGF-1R via monoclonal antibodies was in focus, which failed because of compensatory activation of IR-A upon inhibition of IGF-1R. Recent studies have demonstrated high homology between IGF-IR and IR particularly in tyrosine kinase domain and targeting both receptors have produced efficient therapeutic approaches such as inhibition of cancer cell cycle proliferation. Herein, we have made an attempt to analyze the unique data set from different chemical classes, containing potent ATP competitors against tyrosine kinase domain. We performed the 2D, 3D quantitative structure–activity relationship (QSAR) studies on inhibitors of these receptors to predict useful pharmacophoric features. We have optimized virtual screening of structurally diverse data set of dual inhibitors of IGF-1R and IR. Based on QSAR studies, we predict potential novel clinical candidates with a demonstrated absorption, distribution, metabolism, elimination, and toxicology (ADMETox) track. We also demonstrated comprehensive analysis of co–crystal complexes along with their inhibitors and built 3D- GRid INdependent Descriptors (GRIND) model to obtain insightful features such as H-bond donors and acceptors, overall topology and Vander Waal volume (vdw_vol) which are found to be responsible for dual inhibition of receptors. These findings lead to further description that Tirofiban, Practolol, Edoxaban, Novobiocin have potential to perform dual inhibition of both targets. • Monoclonal antibodies to neutralise IGF-1R failed due to activation of IR upon IGF-1R inhibition leading. • The study focuses on ATP competitive ligands from different chemicals classes against tyrosine kinase using 2D and 3D QSAR. • Our study predicts Tirofiban, Practolol, Edoxaban and Novobiocin as potential novel candidates for dual inhibition. • We cross validated these ligands by building additional pharmacophores which confirmed the Mathew's correlation at 79%.

Synthesis of acetamidosulfonamide derivatives with antioxidative and QSAR studies.

A series of sixteen acetamidosulfonamide derivatives (1-16) have been synthesized and investigated for their antioxidant (radical scavenging and superoxide dismutase (SOD)) and antimicrobial activities. Most compounds exhibited antioxidant activities in which compound 15 displayed the most potent radical scavenging and SOD activities. Quantitative structure-activity relationship (QSAR) has been studied using multiple linear regression. The constructed QSAR models displayed high correlation coefficient (Q 2 LOO-CV = 0.9708 and 0.8753 for RSA and SOD activities, respectively), but low root mean square error (RMSE LOO-CV = 0.5105 and 1.3571 for RSA and SOD activities, respectively). The structure-activity relationship showed that an ethylene group connected to pyridine ring provided significant antioxidant activities. The QSAR models give insight into the rational designed of eighty new sulfonamides with various electron donating and withdrawing groups. The top five new designed sulfonamides with nitro group are potential antioxidants to be further developed for medicinal applications.

Predicting the aggregation number of cationic surfactants based on ANN-QSAR modeling approaches: understanding the impact of molecular descriptors on aggregation numbers.

In this work, a quantitative structure-activity relationship (QSAR) study is performed on some cationic surfactants to evaluate the relationship between the molecular structures of the compounds with their aggregation numbers (AGGNs) in aqueous solution at 25 °C. An artificial neural network (ANN) model is combined with the QSAR study to predict the aggregation number of the surfactants. In the ANN analysis, four out of more than 3000 molecular descriptors were used as input variables, and the complete set of 41 cationic surfactants was randomly divided into a training set of 29, a test set of 6, and a validation set of 6 molecules. After that, a multiple linear regression (MLR) analysis was utilized to build a linear model using the same descriptors and the results were compared statistically with those of the ANN analysis. The square of the correlation coefficient (R 2) and root mean square error (RMSE) of the ANN and MLR models (for the whole data set) were 0.9392, 7.84, and 0.5010, 22.52, respectively. The results of the comparison revealed the efficiency of ANN in detecting a correlation between the molecular structure of surfactants and their AGGN values with a high predictive power due to the non-linearity in the studied data. Based on the ANN algorithm, the relative importance of the selected descriptors was computed and arranged in the following descending order: H-047 > ESpm12x > JGI6> Mor20p. Then, the QSAR data was interpreted and the impact of each descriptor on the AGGNs of the molecules were thoroughly discussed. The results showed there is a correlation between each selected descriptor and the AGGN values of the surfactants.

A Quantitative Structure Activity Relationship (Qsar) Studies for 2-Oxo-1, 2-Dihydroquinoline-4-Carboxylic Acid Derivatives

A Quantitative Structure Activity Relationship (Qsar) Studies for 2-Oxo-1, 2-Dihydroquinoline-4-Carboxylic Acid Derivatives

Synthesis, Antifungal Activity, Three-Dimensional Quantitative Structure-Activity Relationship and Molecular Docking Study of 4-Acyl-3-amino-1,2,4-triazole-thioether Derivatives Containing Natural Pinene Structure

Synthesis, Antifungal Activity, Three-Dimensional Quantitative Structure-Activity Relationship and Molecular Docking Study of 4-Acyl-3-amino-1,2,4-triazole-thioether Derivatives Containing Natural Pinene Structure

Quantitative Structure-Activity Relationship (QSAR) Study of a Series of Chalcone Derivatives Inhibiting Plasmodium Falciparum 3D7

This Quantitative Structure-Activity Relationship (QSAR) study was conducted using a series of twenty (20) chalcone derivatives with inhibitory activities against Plasmodium falciparum 3D7. The molecules were optimized at the B3LYP/LanL2DZ computational level, to obtain the molecular descriptors. This work was performed using the Linear Multiple Regression (LMR) method, the NonLinear Regression (NLMR) and the Artificial Neural Network (ANN) method. These tools allowed us to obtain three (3) quantitative models from the quantum descriptors that are, the overall softness (S), the bond lengths l(c=o) and l(c=c), and the polarizability (α). These models have good statistical performance. Among them, the ANN has a significantly better predictive ability <i>R² =</i>0.997; <I>RMCE</I> = 0.035; <I>F</I>= 3571.499. The external validation tests verify all the criteria of Tropsha <i>et al.</i> and Roy <i>et al.</i> Also, the applicability domain of this model determined from the levers shows that a prediction of the pIC50 of new chalcone derivatives is acceptable when its lever value is lower than 1.07. For the ANN method, the Ch19 molecule is certainly outside the applicability domain, but it is not an influential point for the model, because this derivative belongs to the validation set, and therefore was not used in the model development. The behavior of this molecule could be explained by its structural diversity.

Chemical Applicability of Newly Introduced Topological Invariants and Their Relation with Polycyclic Compounds

In quantitative structure‐property and structure‐activity relationships studies, several graph invariants, namely, topological indices have been defined and studied due to their numerous applications in computer networks, biotechnology, and nanochemistry. Topological indices are numeric parameters that describe the biological, physical, and chemical properties depending on the structure and topology of different chemical compounds. In this article, we inaugurated some degree‐based novel indices, namely, geometric‐harmonic GHI, harmonic‐geometric (HGI), neighborhood harmonic‐geometric (NHGI), and neighborhood geometric‐harmonic (NGHI) indices and verified their chemical applicability. Furthermore, an attempt is made to calculate analytical closed formulas for different variants of silicon carbides and analyze the obtained results graphically.

On ev and ve-Degree Based Topological Indices of Silicon Carbides

In quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies, computation of topological indices is a vital tool to predict biochemical and physio-chemical properties of chemical structures. Numerous topological indices have been inaugurated to describe different topological features. The ev and ve-degree are recently introduced novelties, having stronger prediction ability. In this article, we derive formulae of the ev-degree and ve-degree based topological indices for chemical structure of <i>Si</i>₂<i>C</i>₃ − <i>I</i>[<i>a</i>,<i>b</i>].

Quantitative structure–activity relationship modeling of the inhibitory activities of sulfonylurea herbicides and proposition of new derivatives

The quantitative structure–activity relationship study of the herbicidal activity of a series of sulfonylurea derivatives against Brassica napus is reported herein.

Quantitative structure-activity relationship study of skin sensitization of Michael acceptors based on quantum chemical descriptors

The skin sensitization potential has been scrutinized using density functional theory (DFT) based descriptors within the structural-activity relationship (SAR) parlance. These are the most recurrently used chemical reactivity descriptors to explore the various molecular reactivity studies. For this, a model system consisting of 30 different Michael acceptors whose activity is depicted in terms of pEC3 was taken into account. The usefulness of these descriptors in Quantitative Structure-Activity Relationship (QSAR) research is demonstrated by a good correlation. The statistical results showed that using global softness as a quantum chemical descriptor, the sensitization potential can be adequately explained. It can be concluded from the results that the DFT-based descriptors may be suitable to enlighten effectively the skin sensitization potential of Michael acceptor molecules.

Density functional theory calculations and molecular docking of 2-phenylbenzimidazoles with estrogen receptor for quantitative structure-activity relationship studies

Benzimidazole derivatives, especially 2-phenylbenzimidazole with various substituents on the C-5, C-2 and C-6 positions, are so important in pharmaceutical chemistry. Multiple linear regression was applied to predict the activity of 27 novel 2-phenylbenzimidazole derivatives as anticancer agents. At first, we made an effort to create a QSAR model for a selected series of novel 2-phenylbenzimidazole with density functional theory and molecular docking descriptors. Then, we tried to investigate the nature of the interactions between 2-phenylbenzimidazole derivatives and the estrogen receptor using the molecular docking method. Six descriptors of MATS4e, GATS5e, R6v, R1v+, dipole moment, and torsional free energy were selected for modelling. Due to docking results, increase in the binding energy, and decrease in the dipole moment could increase inhibitor activity.

TIMP-1-expressing breast tumor spheroids for the evaluation of drug penetration and efficacy.

Abundance of stromal cells and extracellular matrix (ECM) is observed in breast cancer, acting as a barrier for drug penetration and presenting a key issue for developing efficient therapeutics. In this study, we aimed to develop a three‐dimensional (3D) multicellular tumor model comprising cancer and stromal cells that could effectively mimic the drug resistance properties of breast cancer. Three different types of spheroid models were designed by co‐culturing breast cancer cells (MDA‐MB‐231) with three different types of stromal cells: human adipose‐derived stromal cells (hASCs), human bone marrow stromal cells, or human dermal fibroblasts. Compared with other models, in the hASC co‐culture model, tissue inhibitor of metalloproteinases‐1 (TIMP‐1) was highly expressed and the activity of matrix metalloproteinases was decreased, resulting in a higher ECM deposition on the spheroid surfaces. This spheroid model showed less drug penetration and treatment efficacy than the other models. TIMP‐1 silencing in hASCs reduced ECM protein expression and increased drug penetration and vulnerability. A quantitative structure–activity relationship study using multiple linear regression drew linear relationships between the chemical properties of drugs and experimentally determined permeability values. Drugs that did not match the drug‐likeness rules exhibited lower permeability in the 3D tumor model. Taken together, our findings indicate that this 3D multicellular tumor model may be used as a reliable platform for efficiently screening therapeutics agents for solid tumors.

Integrating Incompatible Assay Data Sets with Deep Preference Learning.

A large amount of bioactivity assay data is already accumulated in public databases, but the integration of these data sets for quantitative structure–activity relationship (QSAR) studies is not straightforward due to differences in experimental methods and settings. We present an efficient deep-learning-based approach called Deep Preference Data Integration (DPDI). For integrating outcome variables of different assay types, a surrogate variable is introduced, and a neural network is trained such that the total order induced by the surrogate variable is maximally consistent with given data sets. In a task of predicting efficacy of factor Xa inhibitors, DPDI successfully integrated 2959 molecules distributed in 129 assay data sets. In most of our experiments, data integration improved prediction accuracy strongly in interpolation and extrapolation tasks, indicating that DPDI is an effective tool for QSAR studies.