Theoretical Molecular Descriptors Research Articles

Binding Constants of Substituted Benzoic Acids with Bovine Serum Albumin.

Experimental data on the affinity of various substances to albumin are essential for the development of empirical models to predict plasma binding of drug candidates. Binding of 24 substituted benzoic acid anions to bovine serum albumin was studied using spectrofluorimetric titration. The equilibrium constants of binding at 298 K were determined according to 1:1 complex formation model. The relationships between the ligand structure and albumin affinity are analyzed. The binding constant values for m- and p-monosubstituted acids show a good correlation with the Hammett constants of substituents. Two- and three-parameter quantitative structure–activity relationship (QSAR) models with theoretical molecular descriptors are able to satisfactorily describe the obtained values for the whole set of acids. It is shown that the electron-density distribution in the aromatic ring exerts crucial influence on the albumin affinity.

Study on the interaction of anti-inflammatory drugs with human serum albumin using molecular docking, quantitative structure-activity relationship, and fluorescence spectroscopy.

The interaction of 14 anti-inflammatory drugs with human serum albumin (HSA) was investigated using fluorescence quenching, molecular docking studies, and quantitative structure-activity relationship (QSAR) methodology. Binding of anti-inflammatory drugs to HSA plays a fundamental role in their transport, distribution, delivery, and elimination. Binding constants of these drugs to HSA, obtained using the fluorescence quenching method, were within the range 0.01×104 M-1 (acetaminophen) to 1881.05×104 M-1 (meloxicam). Binding sites and binding constants of these anti-inflammatory drugs were estimated using molecular docking. Inspection of the obtained values for docking score, logKb and Kb , showed that the drugs in this data set have a relatively strong binding constant for HSA. QSAR modelling was applied for binding constants obtained from fluorescence quenching and theoretical molecular descriptors. This modelling led to a linear two-parameter model with a correlation coefficient of 0.95 and adequate robustness. The descriptor results showed the importance of a bonding network and electronegativity as the discriminative structural factors in binding affinity for the HSA molecule.

Rate constants of hydroxyl radicals reaction with different dissociation species of fluoroquinolones and sulfonamides: Combined experimental and QSAR studies

Hydroxyl radicals (·OH) initiated degradation is an important process governing fate of aquatic organic micropollutants (OMPs). However, rate constants for aqueous reaction of OMPs with ·OH (kOH) are available only for a limited number of OMPs, which complicates fate assessment of OMPs. Furthermore, molecular structures of many OMPs contain ionizable groups, and the OMPs may dissociate into different anionic/cationic species with different reactivity towards ·OH. Therefore, it is of importance to determine kOH of ionizable OMPs, and to develop quantitative structure-activity relationship (QSAR) models for predicting kOH of OMPs at different ionization forms. Herein kOH values of 9 fluoroquinolones (FQs) and 11 sulfonamides (SAs) at 3 dissociation forms (FQ±/FQ+/FQ−, SA0/SA+/SA−) were determined by competition kinetics experiments. A QSAR model using theoretical molecular structural descriptors was subsequently developed. The QSAR model successfully corroborated previous experimental results, exhibited good statistical performance, and is capable to predict kOH for FQs and SAs with different dissociation forms at environmentally relevant pH conditions. As organic ions have rarely been included in previous QSAR studies, the newly developed model that covers both neutral molecules and ions is of significance for future QSAR development as well as fate assessment of ionizable OMPs.

Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants.

Retention in gas–liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure–retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas–liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.

Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System.

Permeability is used to describe and evaluate the absorption of drug substances in the human gastrointestinal tract (GIT). Permeability is largely dependent on fluctuating pH that causes the ionization of drug substances and also influences regional absorption in the GIT. Therefore, classification models that characterize permeability at wide ranges of pH were derived in the current study. For this, drug substances were described with six data series that were measured with a parallel artificial membrane permeability assay (PAMPA), including a permeability profile at four pH values (3, 5, 7.4, and 9), and the highest and intrinsic membrane permeability. Logistic regression classification models were developed and compared by using two distinct sets of descriptors: (1) a hydrophobicity descriptor, the logarithm of the octanol-water partition (logPow) or distribution (logD) coefficient and (2) theoretical molecular descriptors. In both cases, models have good classification and descriptive capabilities for the training set (accuracy: 0.76-0.91). Triple validation with three sets of drug substances shows good prediction capability for all models: validation set (accuracy: 0.73-0.91), external validation set (accuracy: 0.72-0.9), and the permeability classes of FDA reference drugs for the biopharmaceutical classification system (BCS) (accuracy: 0.72-0.88). The identification of BCS permeability classes was further improved with decision trees that consolidated predictions from models with each descriptor type. These decision trees have higher confidence and accuracy (0.91 for theoretical molecular descriptors and 0.81 for hydrophobicity descriptors) than the individual models in assigning drug substances into BCS permeability classes. A detailed analysis of classification models and related decision trees suggests that they are suitable for predicting classes of permeability for passively transported drug substances, including specifically within the BCS framework. All developed models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.206 ).

Modeling adsorption of organic pollutants onto single-walled carbon nanotubes with theoretical molecular descriptors using MLR and SVM algorithms

Prediction of adsorption equilibrium coefficients (K) of organic compounds onto single walled carbon nanotubes (SWNTs) from in silico molecular descriptors is of importance for probing potential applications of SWNTs as well as for evaluating environmental behavior and ecological risks of organic pollutants and SWNTs. In this study, two models for predicting logK were developed with multiple linear regression (MLR) and support vector machine (SVM) algorithms. The two models have satisfactory goodness-of-fit, robustness and predictive ability, and the SVM model performs slightly better than the MLR model. The two models are based on the up-to-date experimental dataset consisting of 61 logK values, and the applicability domains cover diverse organic compounds with functional groups > CC<, CC, C6H5, >CO, COOH, C(O)O, OH, O, F, Cl, Br, NH2, NH, >N, >NN<, NO2, >NC(O)NH2, >NC(O)NH, S and S(O)(O). The adsorption of organic compounds toward SWNTs is mainly determined by van der Waals forces and hydrophobic interactions. Since only in silico molecular descriptors were employed for the modeling, the developed models are beneficial for prediction purposes.

Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure

In this article, Quantitative Structure Activity Relationships (QSAR) were generated to link the structure of over 120 heterogeneous drugs to rat hepatotoxicity. Existing studies, performed on the same data set, could not highlight relevant structure-activity relationships, and suggested models for the prediction of hepatotoxicity based on genomic data. Binary activity responses were used for the development of classification QSARs using theoretical molecular descriptors calculated with the software PaDEL-Descriptor. A statistically powerful QSAR based on six descriptors was generated by using k-Nearest Neighbour (k-NN) method and by applying the Genetic Algorithm (GA) as variable selection procedure. The new k-NN QSAR outperforms published models by providing better accuracy and less false negatives. This model is a valid alternative to approaches based on genomic descriptors, which cannot be used in virtual screening of new compounds (pre- or post-synthesis) without experimental data.

Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches

Persistent organic products are compounds used for various purposes, such as personal care products, surfactants, colorants, industrial additives, food, pesticides and pharmaceuticals. These substances are constantly introduced into the environment and many of these pollutants are difficult to degrade. Toxic compounds classified as MoA 1 (Mode of Action 1) are low toxicity compounds that comprise nonreactive chemicals. In silico methods such as Quantitative Structure–Activity Relationships (QSARs) have been used to develop important models for prediction in several areas of science, as well as aquatic toxicity studies. The aim of the present study was to build a QSAR model-based set of theoretical Volsurf molecular descriptors using the fish acute toxicity values of compounds defined as MoA 1 to identify the molecular properties related to this mechanism. The selected Partial Least Squares (PLS) results based on the values of cross-validation coefficients of determination (Qcv2) show the following values: Qcv2 = 0.793, coefficient of determination (R2) = 0.823, explained variance in external prediction (Qext2) = 0.87. From the selected descriptors, not only the hydrophobicity is related to the toxicity as already mentioned in previously published studies but other physicochemical properties combined contribute to the activity of these compounds. The symmetric distribution of the hydrophobic moieties in the structure of the compounds as well as the shape, as branched chains, are important features that are related to the toxicity. This information from the model can be useful in predicting so as to minimize the toxicity of organic compounds.

Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination

The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large.In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first local minimum is retained. In this paper, a modification of UVE-PLS is proposed and investigated, in which UVE is repeated until no further reduction in variables is possible, followed by a search for the global RMSECV minimum. The method is called Global-Minimum Error Uninformative-Variable Elimination for PLS, denoted as GME-UVE-PLS or simply GME-UVE. After each iteration, the predictive ability of the PLS model, built with the remaining variable set, is assessed by RMSECV. The variable set with the global RMSECV minimum is then finally selected. The goal is to obtain smaller sets of variables with similar or improved predictability than those from the classical UVE-PLS method.The performance of the GME-UVE-PLS method is investigated using four data sets, i.e. a simulated set, NIR and NMR spectra, and a theoretical molecular descriptors set, resulting in twelve profile-response (X-y) calibrations. The selective and predictive performances of the models resulting from GME-UVE-PLS are statistically compared to those from UVE-PLS and 1-step UVE, one-sided paired t-tests.The results demonstrate that variable reduction with the proposed GME-UVE-PLS method, usually eliminates significantly more variables than the classical UVE-PLS, while the predictive abilities of the resulting models are better. With GME-UVE-PLS, a lower number of uninformative variables, without a chemical meaning for the response, may be retained than with UVE-PLS. The selectivity of the classical UVE method thus can be improved by the application of the proposed GME-UVE method resulting in more parsimonious models.

Least Absolute Deviation Regression and Least Squares for Modeling Retention Indices of Set Compounds Food and Pollutants of the Environment

Considering the importance of the statistical analysis of regression in modeling based separately on study for Quantitative structure retention indices on Carbowax 20 M (ICw20M) and OV-101 columns (IOV-101) relationships (QSRR) are determined for 114 pyrazines. The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied. Least Absolute Deviation regression diagnostics offers alternative dicapproaches whose main feature is the robustness. Here a nonparametric method for detecting influential observations is presented and compared with other classical diagnostics methods. With have been applied for modeling separately retention indices of the same set of (89 pyrazines of Training and 25 of Test) eluted on Columns OV-101 and Carbowax-20M, using theoretical molecular descriptors derived from DRAGON Software and validating the results in the state approached graphically by Probability plot of the error and approached tests statistics of Anderson-Darling, in finished by the confidence interval thanks to robustness concept to check if errors distribution is really approximate.

Synthesis, photodynamic activity, and quantitative structure-activity relationship modelling of a series of BODIPYs

Here we report the synthesis of eleven new BODIPYs (14–24) characterized by the presence of an aromatic ring on the 8 (meso) position and of iodine atoms on the pyrrolic 2,6 positions. These molecules, together with twelve BODIPYs already reported by us (1−12), represent a large panel of BODIPYs showing different atoms or groups as substituent of the aromatic moiety. Two physico-chemical features (1O2 generation rate and lipophilicity), which can play a fundamental role in the outcome as photosensitizers, have been studied. The in vitro photo-induced cell-killing efficacy of 23 PSs was studied on the SKOV3 cell line treating the cells for 24h in the dark then irradiating for 2h with a green LED device (fluence 25.2J/cm2). The cell-killing efficacy was assessed with the MTT test and compared with that one of meso un-substituted compound (13). In order to understand the possible effect of the substituents, a predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, was developed. The results clearly indicate that the presence of an aromatic ring is fundamental for an excellent photodynamic response, whereas the electronic effects and the position of the substituents on the aromatic ring do not influence the photodynamic efficacy.

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the dataset of 1098 chemicals with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2%. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally complex dataset can simplify analysis and interpretation by identifying a subset of the key chemical descriptors associated with broad aquatic toxicity MoAs, and by providing a computational chemistry-based network classification model with reasonable prediction accuracy.

Effect of substituents on prediction of TLC retention of tetra-dentate Schiff bases and their Copper(II) and Nickel(II) complexes.

The objectives of this study were to gain insights into structure-retention relationships and to propose the model to estimating their retention. Chromatographic investigation of series of 36 Schiff bases and their copper(II) and nickel(II) complexes was performed under both normal- and reverse-phase conditions. Chemical structures of the compounds were characterized by molecular descriptors which are calculated from the structure and related to the chromatographic retention parameters by multiple linear regression analysis. Effects of chelation on retention parameters of investigated compounds, under normal- and reverse-phase chromatographic conditions, were analyzed by principal component analysis, quantitative structure-retention relationship and quantitative structure-activity relationship models were developed on the basis of theoretical molecular descriptors, calculated exclusively from molecular structure, and parameters of retention and lipophilicity.

Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity

Active Pharmaceutical Ingredients (APIs) are recognized as Contaminants of Emerging Concern (CEC) since they are detected in the environment in increasing amount, mainly in aquatic compartment, where they may be hazardous for wildlife. The huge lack of experimental data for a large number of end-points requires tools able to quickly highlight the potentially most hazardous and toxic pharmaceuticals, focusing experiments on the prioritized compounds. In silico tools, like QSAR (Quantitative Structure-Activity Relationship) models based on structural molecular descriptors, can predict missing data for toxic end-points necessary to prioritize existing, or even not yet synthesized chemicals for their potential hazard. In the present study, new externally validated QSAR models, specific to predict acute toxicity of APIs in key organisms of the three main aquatic trophic levels, i.e. algae, Daphnia and two species of fish, were developed using the QSARINS software. These Multiple Linear regressions - Ordinary Least Squares (MLR-OLS) models are based on theoretical molecular descriptors calculated by free PaDEL-Descriptor software and selected by Genetic Algorithm. The models are statistically robust, externally predictive and characterized by a wide structural applicability domain. They were applied to predict acute toxicity for a large set of APIs without experimental data. Then predictions were processed by Principal Component Analysis (PCA) and a trend, driven by the combination of toxicities for all the studied organisms, was highlighted. This trend, named Aquatic Toxicity Index (ATI), allowed the raking of pharmaceuticals according to their potential toxicity upon the whole aquatic environment. Finally a QSAR model for the prediction of this Aquatic Toxicity Index (ATI) was proposed to be applicable in QSARINS for the screening of existing APIs for their potential hazard and the a priori chemical design of not environmentally hazardous APIs.

Efficient way of designing fullerene derivatives based on simplified DFT calculations and QSPR modeling

The influence of molecular structure of cyclopropene derivatives on solubility of C60 and C70 methanofullerenes is presented in the framework of quantitative structure–property relationship (QSPR) modeling. The best subset of molecular descriptors was selected with genetic algorithm (GA) from a variety of theoretical molecular descriptors, calculated for the chemical structures of cyclopropane derivatives. The QSPR model developed with multiple linear regression (MLR) was characterized by high robustness (Q2CV=0.86) and the external predictivity (Q2EXT=0.75). The external validated model has been applied to predict solubility (S) of fullerene derivatives for which experimentally determined values of S have been unavailable. Since the two descriptors utilized by the QSPR model are calculated for relatively small structural fragments, the application of this approach does not require extensive and costly quantum-mechanical calculations to be performed for the whole molecular structures of C60 and C70 fullerene derivatives. We believe that both the finally obtained nano-QSPR model and the suggestions considering the dependence of the solubility of fullerene derivatives in chlorobenzene on the molecular structures of the substituents will find practical applications in designing new materials.

In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors.

There are very few studies for combinatorial library design and high throughput screening of 4-anilinoquinoline antimalarial compounds having activities against parasitic strain of P. falciparum. Therefore, an attempt has been made in the present paper to design potent lead compounds in this congener utilizing quantitative structure activity relationship utilizing theoretical molecular descriptors. QSAR models for a series of 4-anilinoquinolines considering various theoretical molecular descriptors including topological, constitutional, geometrical, functional group and atom-centered fragments has been carried out by stepwise forward–backward variable selections assimilating multiple linear regression (MLR) methods showing the topological indices contribute maximum impact on parasitic P. falciparum strain. A combinatorial library of 2160 compounds has been generated and finally, 16 compounds were screened through high throughput screening as promising 4-anilinoquinoline antimalarial hits based on their predicted activities utilizing topological descriptor based validated QSAR model. Highly predicted active compounds were then undergone for pharmacophore modeling to predict mode of binding and to optimize leads having greater affinity towards malarial P. falciparum parasitic strain.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-1593-3) contains supplementary material, which is available to authorized users.

Structural features of substituted triazole-linked chalcone derivatives as antimalarial activities against D10 strains of Plasmodium falciparum: A QSAR approach

A quantitative structure–activity relationship (QSAR) was performed to analyze antimalarial activities against the D10 strains of Plasmodium falciparum of triazole-linked chalcone and dienone hybrid derivatives using partial least squares regression coupled with stepwise forward–backward variable selection method. QSAR analyses were performed on the available IC50 D10 strains of Plasmodium falciparum data based on theoretical molecular descriptors. The QSAR model developed gave good predictive correlation coefficient (r2) of 0.8994, significant cross validated correlation coefficient (q2) of 0.7689, r2 for external test set (rpred2) of 0.8256, coefficient of correlation of predicted data set (rpred,se2) rpred,se of 0.3276. The model shows that antimalarial activity is greatly affected by donor and electron-withdrawing substituents. The study implicates that chalcone and dienone rings should have strong donor and electron-withdrawing substituents as they increase the activity of chalcone. Results show that the predictive ability of the model is satisfactory, and it can be used for designing similar group of antimalarial compounds. The findings derived from this analysis along with other molecular modeling studies will be helpful in designing of the new potent antimalarial activity of clinical utility.

Theoretical Studies of Substituted N-(1H Benzimidazol-2ylmethyl)-5,6,7,8-Tetrahydro-8-Quinolinamines as CXCR4 Antagonists: QSAR Approach

To identify the essential structural requirements in 2D chemical space for the modulation of the CXCR4 antagonists of Substituted N-(1H-benzimidazol-2ylmethyl)5,6,7,8-tetrahydro-8-quinolinamines. The statistically significant model showed an internal predictive power of 81 % and a predictivity for the external test set of about 79%. It reveals that carbon chain connected should be directly attached with benzimidazole ring for maximal determining activity. Three QSAR models were developed for substituted tetrahydro-8-quinolinamines derivatives based on theoretical molecular descriptors calculated solely from the structures of substituted tetrahydro-8-quinolinamines compounds. The QSAR results showed satisfactory statistical quality.

QSAR and pharmacophore modeling of indole-based C-3 pyridone compounds as HCV NS5B polymerase inhibitors utilizing computed molecular descriptors

A series of indole-based C-3 pyridone compounds active against Hepatitis C virus as HCV NS5B polymerase inhibitors have been explored under the framework of quantitative structure–activity relationships (QSARs) and pharmacophore modeling in the present article. QSAR models were developed by considering various kinds of theoretical molecular descriptors including constitutional and geometrical, topological, functional group, and atom-centered fragments indices computed solely from the structure of indole-based C-3 pyridone compounds utilizing stepwise forward−backward variable selections incorporated in multiple linear regression (MLR) methods. MLR shows that topological indices can contribute the maximum impact on biological activity obtained in terms of model quality parameters, such as R 2 = 0.946, $$ Q_{\text{Loo}}^{2} $$ = 0.883, and $$ R_{\text{pred}}^{2} $$ = 0.642, respectively. Constitutional and geometrical-based model can produce R 2 = 0.849, $$ Q_{\text{Loo}}^{2} $$ = 0.776, and $$ R_{\text{pred}}^{2} $$ = 0.623, respectively; whereas, model utilizing functional group and atom-centered fragments indices can contribute R 2 = 0.885, $$ Q_{\text{Loo}}^{2} $$ = 0.812, and $$ R_{\text{pred}}^{2} $$ = 0.612, respectively. Prediction of essential structural features is strongly achieved by introducing pharmacophore modeling of these highly active congeners which showed that more number of hydrogen bonding interactions between the substituents associated to the various points of diversity of the indole nucleus including N-1, C-2, and C-3 aromatic groups with the HCV NS5B polymerase target may enhance the biological activities in this congeneric ligands.

The kinetics and QSAR of abiotic reduction of mononitro aromatic compounds catalyzed by activated carbon

The kinetics of abiotic reduction of mono-nitro aromatic compounds (mono-NACs) catalyzed by activated carbon (AC) in an anaerobic system were examined. There were 6 types of substituent groups on nitrobenzene, including methyl, chlorine, amino, carboxyl, hydroxyl and cyanogen groups, at the ortho, meta or para positions. Our results showed that reduction followed pseudo-first order reaction kinetics, and that the rate constant (logkSA) varied widely, ranging between −4.77 and −2.82, depending upon the type and position of the substituent. A quantitative structure–activity relationship (QSAR) model using 15 theoretical molecular descriptors and partial-least-squares (PLS) regression was developed for the reduction rates of mono-NACs catalyzed by AC. The cross-validated regression coefficient (Qcum2, 0.861) and correlation coefficient (R2, 0.898) indicated significantly high robustness of the model. The VIP (variable importance in the projection) values of energy of the lowest unoccupied molecular orbital (ELUMO) and the maximum net atomic charge on the aromatic carbon bound to the nitro group (QC-) were 1.15 and 1.01, respectively. These values indicated that the molecular orbital energies and the atomic net charges might play important roles in the reduction of mono-NACs catalyzed by AC in anaerobic systems.