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ABSTRACT: Little attention has been given to the selection of trial descriptor sets when designing a QSAR analysis even though a great number of descriptor classes, and often a greater number of descriptors within a given class, are now available. This paper reports an effort to explore interrelationships between QSAR models and descriptor sets. Zhou and coworkers (Zhou et al., Nano Letters 8(3) p859-865, 2008)1 designed, synthesized and tested a combinatorial library of 80 surface modified, that is decorated, multi-walled carbon nanotubes for their composite nanotoxicity using six endpoints all based upon a common 0 to 100 activity scale. Each of the six endpoints for the 29 most nanotoxic decorated-nanotubes were incorporated as the training set for this study. The study reported here includes trial descriptor sets for all possible combinations of MOE, VolSurf and 4D-fingerprints (FP) descriptor classes, as well as including and excluding explicit spatial contributions from the nanotube. Optimized QSAR models were constructed from these multiple trial descriptor sets. It was found that a) both the form and quality of the best QSAR models for each of the endpoints are distinct, and, b) some endpoints are quite dependent upon 4D-FP descriptors of the entire nanotube-decorator complex. However, other endpoints yielded equally good models only using decorator descriptors with, and without, the decorator-only 4D-FP descriptors. Lastly, and most importantly, the quality, significance and interpretation of a QSAR model were found to be critically dependent on the trial descriptor sets used within a given QSAR endpoint study.
Journal of Chemical Information and Modeling 12/2012; · 4.68 Impact Factor
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ABSTRACT: Receptor-dependent four-dimensional quantitative structure-activity relationship (RD-4D-QSAR) studies were applied on a series of 21 peptides reversible inhibitors of Trypanosoma cruzi trypanothione reductase (TR) (Amino Acids, 20, 2001, 145). The RD-4D-QSAR (J Chem Inform Comp Sci, 43, 2003, 1591) approach can evaluate multiple conformations from molecular dynamics simulation and several superposition structure alignments inside a box composed by unitary cubic cells. The descriptors are the occupancy frequency of the atoms types inside the grid cells. We could develop 3D-QSAR models that were highly predictive (q(2) above 0.71). The 3D-QSAR models can be visualized as a spatial map of atom types that are important on the comprehension of the ligand-enzyme interaction mechanism, pointing main pharmacophoric groups and TR subsites described in the literature. We were able also to identify some TR subsites for further development in the drug discovery process against tropical diseases not yet studied.
Chemical Biology & Drug Design 01/2012; 79(5):740-8. · 2.28 Impact Factor
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ABSTRACT: The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.
Journal of Computer-Aided Molecular Design 12/2011; 26(1):39-43. · 3.39 Impact Factor
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ABSTRACT: The human ether-a-go-go related gene (hERG) potassium ion channel plays a key role in cardiotoxicity and is therefore a key target as part of preclinical drug discovery toxicity screening. The PubChem hERG Bioassay data set, composed of 1668 compounds, was used to construct an in silico screening model. The corresponding trial models were constructed from a descriptor pool composed of 4D fingerprints (4D-FP) and traditional 2D and 3D VolSurf-like molecular descriptors. A final binary classification model was constructed via a support vector machine (SVM). The resultant model was then validated using the PubChem hERG Bioassay data set (AID 376) and an external hERG data set by evaluating the model's ability to determine hERG blockers from nonblockers. The external data set (the test set) consisted of 356 compounds collected from available literature data and consisting of 287 actives and 69 inactives. Four different sampling protocols and a 10-fold cross-correlation analysis--used in the validation process to evaluate classification models--explored the impact of the active--inactive data imbalance distribution of the PubChem high-throughput data set. Four different data sets were explored, and the one employing Lipinski's rule-of-five coupled with measures of relative molecular lipophilicity performed the best in the 10-fold cross-correlation validation of the training data set as well as overall prediction accuracy of the external test sets. The linear SVM binary classification model building strategy was applied to different combinations of MOE (traditional 2D, "21/2D", and 3D VolSurf-like) and 4D-FP molecular descriptors to further explore and refine previously proposed key descriptors, identify new significant features that contribute to the prediction of hERG toxicity, and construct the optimal SVM binary classification model from a shrunken descriptor pool. The accuracy, sensitivity, and specificity of the best model determined from 10-fold cross-validation are 95, 90, and 96%, respectively; the overall accuracy is near 87% for the external set. The models constructed in this study demonstrate the following: (i) robustness based upon performance in accuracy across the structural diversity of the training set, (ii) ability to predict a compound's "predisposition" to block hERG ion channels, and (iii) define and illustrate structural features that can be overlaid onto the chemical structures to aid in the 3D structure-activity interpretation of the hERG blocking effect.
Chemical Research in Toxicology 06/2011; 24(6):934-49. · 3.78 Impact Factor
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ABSTRACT: Blockage of the human ether-a-go-go related gene (hERG) potassium ion channel is a major factor related to cardiotoxicity. Hence, drugs binding to this channel have become an important biological end point in side effects screening. A set of 250 structurally diverse compounds screened for hERG activity from the literature was assembled using a set of reliability filters. This data set was used to construct a set of two-state hERG QSAR models. The descriptor pool used to construct the models consisted of 4D-fingerprints generated from the thermodynamic distribution of conformer states available to a molecule, 204 traditional 2D descriptors and 76 3D VolSurf-like descriptors computed using the Molecular Operating Environment (MOE) software. One model is a continuous partial least-squares (PLS) QSAR hERG binding model. Another related model is an optimized binary classification QSAR model that classifies compounds as active or inactive. This binary model achieves 91% accuracy over a large range of molecular diversity spanning the training set. Two external test sets were constructed. One test set is the condensed PubChem bioassay database containing 876 compounds, and the other test set consists of 106 additional compounds found in the literature. Both of the test sets were used to validate the binary QSAR model. The binary QSAR model permits a structural interpretation of possible sources for hERG activity. In particular, the presence of a polar negative group at a distance of 6-8 A from a hydrogen bond donor in a compound is predicted to be a quite structure-specific pharmacophore that increases hERG blockage. Since a data set of high chemical diversity was used to construct the binary model, it is applicable for performing general virtual hERG screening.
Journal of Chemical Information and Modeling 07/2010; 50(7):1304-18. · 4.68 Impact Factor
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ABSTRACT: Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. The quantitative structure-activity relationship (QSAR) formalisms are among the most important strategies that can be applied for the successful design new molecules. This review provides a comprehensive review on the evolution and current status of 4D-QSAR, highlighting present challenges and new opportunities in drug design.
Molecules 05/2010; 15(5):3281-94. · 2.39 Impact Factor
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ABSTRACT: Tuberculosis (TB) is the primary cause of mortality among infectious diseases. Mycobacterium tuberculosis monophosphate kinase (TMPKmt) is essential to DNA replication. Thus, this enzyme represents a promising target for developing new drugs against TB. In the present study, the receptor-independent, RI, 4D-QSAR method has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 81 thymidine analogues, and two corresponding subsets, reported as inhibitors of TMPKmt. The resulting optimized models are not only statistically significant with r(2) ranging from 0.83 to 0.92 and q(2) from 0.78 to 0.88, but also are robustly predictive based on test set predictions. The most and the least potent inhibitors in their respective postulated active conformations, derived from each of the models, were docked in the active site of the TMPKmt crystal structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. Moreover, the QSAR models provide insights regarding a probable mechanism of action of the analogues.
Journal of Computer-Aided Molecular Design 03/2010; 24(2):157-72. · 3.39 Impact Factor
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ABSTRACT: The original quantitative structure-activity relationship (QSAR) formulation was proposed by Hansch and Fujita in the 1960's and, since then QSAR analysis has evolved as a mature science, due mainly to the advances that occurred in the past two decades in the fields of molecular modeling, data analysis algorithms, chemoinformatics, and the application of graph theory in chemistry. Moreover, it is also worthy of note the exponential progress that have occurred in software and hardware development. In this context, a myriad of QSAR methods exist; from the considered "classical" approaches (known as two-dimensional (2D) QSAR), to three-dimensional (3D) and multidimensional (nD) QSAR ones. A distinct QSAR approach has been recently proposed, the receptor-dependent-QSAR, where explicit information regarding the receptor structure (usually a protein) is extensively used during modeling process. Indeed, a limited, but growing number of receptor-dependent QSAR methods are reported in the literature. With no intention to be comprehensive, an overview of receptor-dependent QSAR methods will be discussed along with an in-depth examination of their applications in drug design, virtual screen, and ADMET modeling in silico.
Medicinal chemistry (Shāriqah (United Arab Emirates)) 08/2009; 5(4):359-66. · 1.64 Impact Factor
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ABSTRACT: Thymidine monophosphate kinase (TMPK) has emerged as an attractive target for developing inhibitors of Mycobacterium tuberculosis growth. In this study the receptor-independent (RI) 4D-QSAR formalism has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 5'-thiourea-substituted alpha-thymidine inhibitors. Models were developed for the entire training set and for a subset of the training set consisting of the most potent inhibitors. The optimized (RI) 4D-QSAR models are statistically significant (r(2) = 0.90, q(2) = 0.83 entire set, r(2) = 0.86, q(2) = 0.80 high potency subset) and also possess good predictivity based on test set predictions. The most and least potent inhibitors, in their respective postulated active conformations derived from the models, were docked in the active site of the TMPK crystallographic structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. This model identifies new regions of the inhibitors that contain pharmacophore sites, such as the sugar-pyrimidine ring structure and the region of the 5'-arylthiourea moiety. These new regions of the ligands can be further explored and possibly exploited to identify new, novel, and, perhaps, better antituberculosis inhibitors of TMPKmt. Furthermore, the 3D-pharmacophores defined by these models can be used as a starting point for future receptor-dependent antituberculosis drug design as well as to elucidate candidate sites for substituent addition to optimize ADMET properties of analog inhibitors.
Journal of Chemical Information and Modeling 04/2009; 49(4):1070-8. · 4.68 Impact Factor
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ABSTRACT: The ligand-inducible, ecdysteroid receptor (EcR) gene-expression system can add critical control features to protein expression in cell and gene therapy. However, potent natural ecdysteroids possess absorption, distribution, metabolism and excretion (ADME) properties that have not been optimised for use as gene-switch actuators in vivo. Herein we report the first systematic synthetic exploration of ecdysteroids toward modulation of gene-switch potency. Twenty-three semi-synthetic O-alkyl ecdysteroids were assayed in both a natural insect system (Drosophila B(II) cells) and engineered gene-switch systems in mammalian cells using Drosophila melanogaster, Choristoneura fumiferana, and Aedes aegypti EcRs. Gene-switch potency is maintained, or even enhanced, for ecdysteroids methylated at the 22-position in favourable cases. Furthermore, trends toward lower solubility, higher permeability, and higher blood-brain barrier penetration are supported by predicted ADME properties, calculated using the membrane-interaction (MI)-QSAR methodology. The structure-activity relationship (SAR) of alkylated ecdysteroids indicates that 22-OH is an H-bond acceptor, 25-OH is most likely an H-bond donor, and 2-OH and 3-OH are donors and/or acceptors in network with each other, and with the EcR. The strategy of alkylation points the way to improved ecdysteroidal actuators for switch-activated gene therapy.
ChemMedChem 01/2009; 4(1):55-68. · 3.15 Impact Factor
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Journal of Chemical Information and Modeling 01/2009; 49(1):1-5. · 4.68 Impact Factor
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05/2008: pages 1576 - 1603; , ISBN: 9783527618279
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ABSTRACT: Four possible sources of cellular toxicity due to the insertion of a carbon nanotube into a dimyristoylphosphatidylcholine (DMPC) membrane bilayer were explored using the membrane interaction quantitative structure-activity relationship methodology. Comparisons of (i) the structural organization of the membrane bilayer, (ii) dynamical features of the membrane bilayer, and (iii) transport of small polar molecules across the membrane bilayer were carried out with, and without, a carbon nanotube inserted into the bilayer. A fourth study was performed to determine how the transport of solvated ions through the inserted nanotube might alter the structure of the membrane bilayer. Two large changes in the bilayer occur due to insertion of the carbon nanotube. First, there is an alteration in the packing of the DMPC bilayer molecules, which extends at least 18 A from the nanotube, and includes the creation of a relatively open, unoccupied cylindrical ring of 2-4 A thickness directly around the nanotube. Second, the same bilayer structure, which undergoes the change in structural organization, also becomes much more rigid than when the nanotube is not inserted. Solvated calcium ions are predicted to preferentially transport through the inserted nanotube as compared to hydrated sodium ions, but the solvated calcium ion also produces an alteration in the local bilayer structure as it passes through the nanotube. The total diffusion coefficient of ethanol through the membrane bilayer increases by about 35% in the presence of the inserted nanotube. Urea and caffeine also undergo increases in their diffusion coefficients for transport through the bilayer, due to the inserted nanotube, but these increases are less than that of ethanol. Each of the three penetrants also diffuses more directly through the membrane bilayer in the presence of the nanotube, especially caffeine and urea.
Chemical Research in Toxicology 03/2008; 21(2):459-66. · 3.78 Impact Factor
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ABSTRACT: A set of 30 structurally diverse molecules, for which Caco-2 cell permeation coefficients were determined, formed the training set for construction of Caco-2 cell permeation models based upon membrane-interaction (MI) QSAR analysis and a new QSAR method called 4D-fingerprint QSAR analysis. The descriptor terms of the 4D-fingerprints equation are molecular similarity eigenvalues, and this set of descriptors is being evaluated as a potential "universal" QSAR descriptor set. The 4D-fingerprint model suggests that Caco-2 cell permeation is governed by the spatial distribution of hydrogen bonding and nonpolar groups over the molecular shape of a molecule. Moreover, a complementary resampling of the original Caco-2 cell permeation training set, followed by the construction of several "clustered" MI-QSAR models, led to a consensus model consistent in interpretation with the 4D-fingerprint model.
Journal of Pharmaceutical Sciences 02/2008; 97(1):566-83. · 3.06 Impact Factor
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ABSTRACT: Three and four state categorical quantitative structure-activity relationship (QSAR) models for skin sensitization have been constructed using data from the murine Local Lymph Node Assay studies. These are the same data we previously used to build two-state (sensitizer, nonsensitizer) QSAR models (Li et al., 2007, Chem. Res. Toxicol. 20, 114-128). 4D-fingerprint descriptors derived from the 4D-molecular similarity paradigm are used to generate these models. A training set of 196 and a test set of 22 structurally diverse compounds were used in this study. Logistic regression, and partial least square coupled logistic regression were used to build the models. The three-state QSAR model gives a classification accuracy of 73.4% for the training set and 63.6% for the test set, while the random average value of classification accuracy for any three-state data set is 33.3%. The two-2-state [four categories in total] QSAR model gives a classification accuracy of 83.2% for the training set and 54.6% for the test set, while the random average value of classification accuracy for any two-2-state data set is 25%. An analysis of the skin-sensitization models developed in this study, as well as the two-state QSAR models developed in our previous analysis, suggests that the "moderate" sensitizers may be the main source of limited model accuracy.
Toxicological Sciences 11/2007; 99(2):532-44. · 4.65 Impact Factor
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ABSTRACT: Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the local lymph node assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, for eaxample, quantitative structure-activity relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR) and partial least-square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, X(2)HL, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, whereas that of the PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0% to 86.7%, whereas that of the PLS-logistic regression models ranges from 73.3% to 80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors, and negatively partially charged atoms.
Chemical Research in Toxicology 02/2007; 20(1):114-28. · 3.78 Impact Factor
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ABSTRACT: The p38-mitogen-activated protein kinases (p38-MAPKs) belong to a family of serine-threonine kinases activated by pro-inflammatory or stressful stimuli that are known to be involved in several diseases. Their biological importance, related to the release of inflammatory pro-cytokines such as tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1), has generated many studies aiming at the development of selective inhibitors for the treatment of inflammatory diseases. In this work, we developed receptor-based three dimensional (3D) quantitative structure-activity relationship (QSAR) models for a series of 33 pyridinyl imidazole compounds [Liverton et al. (1999) 42:2180], using a methodology named free-energy force-field (FEFF) [Tokarski and Hopfinger (1997) 37:792], in which scaled intra- and intermolecular energy terms of the Assisted Model Building Energy Refinement (AMBER) force field combined with a hydration-shell solvation model are the independent variables used in the QSAR studies. Multiple temperature molecular-dynamics simulations (MDS) of ligand-protein complexes and genetic-function approximation (GFA) were employed using partial least squares (PLS) as the fitting functions to develop FEFF-3D-QSAR models for the binding process. The best model obtained in the FEFF-3D-QSAR receptor-dependent (RD) method shows the importance of the van der Waals energy change upon binding and the electrostatic energy in the interaction of ligands with the receptor. The QSAR equations described here show good predictability and may be regarded as representatives of the binding process of ligands to p38-MAPK. Additionally, we have compared the top FEFF-3D-QSAR model with receptor independent (RI) 4D-QSAR models developed in a recent study [Romeiro et al. (2005) 19:385].
Journal of Molecular Modeling 10/2006; 12(6):855-68. · 1.80 Impact Factor
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ABSTRACT: Based on 2D-connectivity molecular similarity and cluster analyses, a dataset for HSA binding is divided into the training set and the test set. 4D-fingerprint similarity measures were applied to this dataset. Four different predictive schemes (SM, SA, SR, and SC) were applied to the test set based on the similarity measures of each compound to the compounds in the training set. The first algorithmic scheme (SM), which only takes the most similar compound in the training set into consideration, predicts the binding affinity of a test compound. This scheme has relatively poor predictivity based on 4D-fingerprint similarity analyses. The other three algorithmic schemes (SM, SR, and SC), which assign a weighting coefficient to each of the top-ten most similar training set compounds, have reasonable predictivity of a test set. The algorithmic scheme which categorizes the most similar compounds into different weighted clusters predicts the test set best. The 4D-fingerprints provide 36 different individual IPE/IPE type molecular similarity measures. Further investigation shows that the NP/HA, HS/HA, and HA/HA IPE/IPE type measures predict the test set well. Moreover, these three IPE/IPE type similarity measures are very similar to one another for the particular training and test sets investigated. The 4D-fingerprints have relatively high predictivity for this particular dataset.
Bioorganic & Medicinal Chemistry 03/2006; 14(3):611-21. · 2.92 Impact Factor
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ABSTRACT: Ligand-receptor molecular dynamics simulations (MDS) were carried out for a set of hydrazides bound to the enoyl-acp reductase from M. tuberculosis, InhA (PDB entry code 1zid). The hypothesized active conformations resulting from a previous receptor-independent (RI) 4D-QSAR analysis and related optimum model/alignment were used in this study. The molecular dynamics simulations (MDS) protocol employed 500000 steps for each ligand-receptor complex, the step size was 0.001 ps (1 fs), and the simulation temperature was 310 K, the same temperature used in the biological assay. An output trajectory file was saved every 20 simulation steps, resulting in 25,000 conformations. The hydration shell model was used to calculate the solvation energy of the lowest-energy conformation obtained from each MDS. Structural parameters as well as binding energy contributions were considered in this analysis. The thermodynamic descriptors ELE1,4, ELtors, ELvdW, ELel, and ELel+Hb appear to be more relevant to the biological activity. These findings can be meaningful for developing QSAR studies and for designing new antituberculosis agents. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2006
International Journal of Quantum Chemistry 12/2005; 106(13):2689 - 2699. · 1.36 Impact Factor
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ABSTRACT: Membrane-interaction quantitative structure activity relationship (MI-QSAR) analysis was applied to a data set with 18 compounds in 18 different membranes. MI-QSAR was used to estimate the ADMET properties including the transport of organic solutes through biological membranes. The most important descriptors are the aqueous solvation free energy, FH2O, and diffusion coefficient for all membranes. The correlation coefficient, r2, and cross-validation correlation coefficient, q2, for DMPG membrane is 0.850 and 0.770, respectively. The relationship between FH2O and permeability is nonlinear. But the detail effect of aqueous solvation free energy and diffusion coefficient to the permeability depends on the type of membrane. The final models also support the solution-diffusion mechanism of transport is important in membrane.
International Journal of Pharmaceutics 12/2005; 304(1-2):115-23. · 3.35 Impact Factor
