Comprehensive Descriptors For Structural And Statistical Analysis Research Articles

Modeling of hygroscopicity parameter kappa of organic aerosols using quantitative structure-property relationships

The hygroscopicity of organic aerosol in the atmosphere can be represented by a semi-empirical single parameter, κ. In this work we test possibilities for developing quantitative structure-property relationship (QSPR) models for κ based on chemical similarity. Models were developed in two ways: by manually assessing the suitability of several plausible physico-chemical descriptors; and by systematically evaluating hundreds of constitutional (e.g. number of particular atoms, bond types, molecular weight,...), topological, electrostatic, geometrical and quantum-chemical descriptors with the QSPR modelling software CODESSA (COmprehensive DEscriptors for Structural and Statistical Analysis). A set of 74 compounds with measured κ values was taken from the literature and prediction capabilities of the developed models were evaluated by leave-one-out cross-validation procedure. A 5-parameter linear regression model obtained with CODESSA was found to be the most suitable. Among the five descriptors, the two providing the highest contributions to the total variance were found to be (i) the final heat of formation divided by the number of atoms (69 %) and (ii) the ratio of molecular weight and molecular volume (16 %), although other topological and electrostatic descriptors were also of non-negligible importance for prediction of κ. The squared correlation coefficient and the root mean square error of a leave-one-out cross-validation procedure were 0.80 and 0.037, respectively. The results show that quantitative structure-property relationship approaches are useful for modeling κ.

Development of quantitative structure activity relationships for the binding affinity of methoxypyridinium cations for human acetylcholinesterase

Among the most toxic substances known are the organophosphorus (OP) compounds used as pesticides and chemical warfare agents. Owing to their high toxicity there is a number of efforts underway to develop effective therapies for OP agent exposure. To date all therapies in use treat inhibited acetylcholinesterase (AChE), but are ineffective for the treatment of inhibited AChE, which has undergone a subsequent hydrolysis process, referred to as aging. Toward developing a therapy for treating victims of OP intoxication in the aged state we have developed Quantitative Structure–Activity Relationships (QSARs) based on the AM1 semiempirical quantum mechanical method using the program, CODESSA (COmprehensive Descriptors for Structural and Statistical Analysis). Using this methodology we obtained a multiple correlation QSAR equation which gave R2=0.9359 for a random training set of 38 ligands and R2=0.9236 for prediction on a random test set of 9 ligands.

Linear and non-linear QSAR modelling of juvenile hormone esterase inhibitors

A tight control of juvenile hormone (JH) titre is crucial during the life cycle of a holometabolous insect. JH metabolism is made through the action of enzymes, particularly the juvenile hormone esterase (JHE). Trifluoromethylketones (TFKs) are able to inhibit this enzyme to disrupt the endocrine function of the targeted insect. In this context, a set of 96 TFKs, tested on Trichoplusia ni for their JHE inhibition, was split into a training set (n = 77) and a test set (n = 19) to derive a QSAR model. TFKs were initially described by 42 CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) descriptors, but a feature selection process allowed us to consider only five descriptors encoding the structural characteristics of the TFKs and their reactivity. A classical and spline regression analysis, a three-layer perceptron, a radial basis function network and a support vector regression were experienced as statistical tools. The best results were obtained with the support vector regression (r 2 and = 0.91). The model provides information on the structural features and properties responsible for the high JHE inhibition activity of TFKs.

Analysis of ammonia and aliphatic amines in environmental water by micellar electrokinetic chromatography and QSPR modeling of electrophoretic migration time

In this paper, a rapid and sensitive method for the determination of ammonia and 22 aliphatic amines derivatized was established by cyclodextrin (CD) modified micellar electrokinetic chromatography with laser-induced fluorescence detection using 4-chloro-7-nitrobenzo-2-oxa-1,3-diazole as a derived reagent. By a series of unvaried studies, the best conditions were selected using 20 mM tetraborate (pH 9.70) containing 25 mM SDS, 7 mM β-CD, 10% acetonitrile, 0.5 M urea, and applied voltage 25 kV. Under the optimum conditions, ammonia and 22 aliphatic amines were separated successfully in 19 min. The signal response was linear over three-order of concentrations. The intra-day and inter-day relative standard deviations (RSD) were less than 0.81% and 1.00% for migration times, 1.76% and 2.01% for peak areas. Limits of detection (LOD) for the analytes was (S/N = 3) approximate 10 −9–10 −12 M. The method was applied to the determination of ammonia and aliphatic amines in environmental water with recoveries in the range of 90.2–110.8%. In addition, a quantitative structure–property relationship (QSPR) model for the estimation of the electrophoretic migration time was established with the comprehensive descriptors for structural and statistical analysis (CODESSA) program for the first time. The multilinear regression (MLR) equation contained two theoretical descriptors and had the following statistical indices: R 2 = 0.9668, F = 290.7839, R cv 2 = 0.9576 and S 2 = 0.8149.

Comparative QSTR Study Using Semi-Empirical and First Principle Methods Based Descriptors for Acute Toxicity of Diverse Organic Compounds to the Fathead Minnow

Several quantum-mechanics-based descriptors were derived for a diverse set of 48 organic compounds using AM1, PM3, HF/6-31+G, and DFT-B3LYP/6-31+G (d) level of the theory. LC50 values of acute toxicity of the compounds were correlated to the fathead minnow and predicted using calculated descriptors by employing Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA) program. The heuristic method, implemented in the CODESSA program for selecting the ‘best’ regression model, was applied to a pre-selection of the most-representative descriptors by sequentially eliminating descriptors that did not satisfy a certain level of statistical criterion. First model, statistically, the most significant one has been drawn up with the help of DFT calculations in which the squared correlation coefficient R2 is 0.85, and the squared cross-validation correlation coefficient is 0.79. Second model, which has been drawn up with the help of HF calculations, has its statistical quality very close to the DFT-based one and in this model value of R2 is 0.84 and that of is 0.78. Third and fourth models have been drawn up with the help of AM1 and PM3 calculations, respectively. The values of R2 and in the third case are correspondingly 0.79 and 0.66, whereas in the fourth case they are 0.78 and 0.65 respectively. Results of this study clearly demonstrate that for the calculations of descriptors in modeling of acute toxicity of organic compounds to the fathead minnow, first principal methods are much more useful than semi-empirical methods.

QSPR modeling of stability constants of diverse 15-crown-5 ethers complexes using best multiple linear regression

Quantitative structure–property relationships (QSPR) models for the stability constants of 58 complexes of 1,4,7,10,13-pentaoxacyclopentadecane ethers (15C5) were established with the Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA) program. Experimental stability constants (log K) for a diverse set of 58 complexes of 15C5 structures are correlated with computed structural descriptors using CODESSA. Stability constants for complexes of 15C5 ethers with potassium cation (K+) have been determined at 25 °C in methanol solution. Standard quantum chemistry packages are used for optimizing the molecular geometry and for semi-empirical quantum computations. The QSPR model for the stability constants (log K) is obtained by selecting descriptors from a wide diversity of constitutional, geometrical, topological, electrostatic, quantum chemical, and thermodynamic molecular descriptors. The best multilinear regression method (BMLR) encoded in CODESSA software was used to select significant descriptors for building multilinear QSPR model and the predictive power of model is estimated with the leave-one-out (LOO) cross-validation method. The proposed model can be used for the prediction of the stability constants of 15C5 complexes. The best QSPR model with five descriptors has R 2 = 0.9452, s 2 = 0.0110, and F = 67.0312.

Molecular modeling studies of pyridopurinone derivatives—Potential phosphodiesterase 5 inhibitors

Two- and three-dimensional quantitative structure-activity relationship (QSAR) and docking studies were carried out on a series of pyridopurinones, to model their phosphodiesterase 5 (PDE5) inhibitory activities. 2D-QSAR was performed using the heuristic and best multi linear regression (BMLR) methods in CODESSA (comprehensive descriptors for structural and statistical analysis), which had given linear models between the inhibitory activity and five descriptors of PDE5 inhibitors, with r 2 = 0.987, 0.987, q 2 = 0.970, 0.970, F = 166.71, 166.71 and s 2 = 0.0004, 0.0176, respectively. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) have provided statistically significant models with q 2 values of 0.784, 0.742 and r 2 values of 0.975, 0.972 respectively. The predictive ability of the models was validated using a set of 6 compounds that were not included in the training set and the predictive r 2 obtained for the test set was 0.901 and 0.888 respectively. Docking studies were employed to determine the probable binding conformation of these analogues in the PDE5 active site using the programs GOLD and AutoDock whose results were found complementary with 3D-QSAR maps. Since the potency towards PDE5 and the selectivity over PDE6 is important for the successful development of new PDE5 inhibitors, a PDE6 homology model was built using Insight II and Modeller with Phi-Psi BLAST alignment. The molecules were docked in the active site of PDE6 and analyzed the probable reasons for selectivity of these molecules towards PDE5 over PDE6. Mapping the 3D-QSAR models to the active site of PDE5 provides a new insight into the protein-inhibitor interactions and helpful in designing potent and selective PDE5 inhibitors for the treatment of erectile dysfunction.

QSPR model of Henry’s law constant for a diverse set of organic chemicals based on genetic algorithm-radial basis function network approach

Six quantitative structure–property relationship (QSPR) models for a diverse set of experimental data of Henry’s law constant ( H) of organic chemicals under environmental condition ( T = 25 °C; water–air system) have been developed based on four different molecular descriptor sets. Three different models based on the descriptors of CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis), Tsar, and Dragon software and a model based on a combined descriptor set from these packages, and in addition from HYBOT software, have been established using the stepwise regression method. The combined descriptors set model gave the best results. Furthermore, a genetic algorithm was used for descriptor selection from a combined set of descriptors, and a radial basis function network was utilized to establish a model with a low root mean square error (RMSE). The results of this study were compared with the well-known bond contribution and group contribution methods. The group contribution method failed to predict Henry’s law constant of 170 from all 940 compounds in the data-set. RMSEs of 0.693, 0.798, and 0.564 were achieved for bond contribution, group contribution and the best QSPR model of this study, respectively, based on logarithm of H. Analysis of different QSPR models showed that hydrogen bonding between the organic solute and water as a solvent has the greatest influence on this partitioning phenomenon.

Quantitative structure–activity relationship for prediction of the toxicity of polybrominated diphenyl ether (PBDE) congeners

Levels of Polybrominated diphenyl ether (PBDEs) are increasing in the environment due to their use as flame retardants. The similarities of structure to polychlorinated biphenyl (PCB) congeners suggest that they may share similar toxicological properties, such as hepatic enzyme induction. In this work, quantitative structure–activity relationship (QSAR) models were constructed based on 406 descriptors for the logarithm of toxicology index (aryl hydrocarbon receptor relative binding affinities, AhR, I) of 18 PBDE congeners. The method used for building model is the Heuristic method, which is included in comprehensive descriptors for structural and statistical analysis (CODESSA) software. The best regression model involved four descriptors, which were related to the conformational changes, atomic reactivity, molecular electrostatic field, and non-uniformity of mass distribution in a molecule of PBDEs, etc. The high square of the correlation coefficient R 2(0.903) showed the model was satisfactory.

Prediction of standard Gibbs energies of the transfer of peptide anions from aqueous solution to nitrobenzene based on support vector machine and the heuristic method

Quantitative structure-property relationship (QSPR) method was performed for the prediction of the standard Gibbs energies (DeltaGtheta) of the transfer of peptide anions from aqueous solution to nitrobenzene. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the peptides. The four molecular descriptors selected by the heuristic method (HM) in COmprehensive DEscriptors for Structural and Statistical Analysis (CODESSA) were used as inputs for support vector machine (SVM) and radial basis function neural networks (RNFNN). The results obtained by the novel machine learning technique, SVM, were compared with those obtained by HM and RBFNN. The root mean squared errors (RMS) of the training, predicted and overall data sets are 2.192, 2.541 and 2.267 unit (kJ/mol) for HM, 1.604, 2.478 and 1.817 unit (kJ/mol) for RBFNN and 1.5621, 2.364 and 1.756 unit (kJ/mol) for SVM, respectively. The prediction results were in agreement with the experimental values. This paper provided a potential method for predicting the physiochemical property (DeltaGtheta) of various small peptides.

Development of quantitative structure gas chromatographic relative retention time models on seven stationary phases for 209 polybrominated diphenyl ether congeners

Quantitative structure retention relationships (QSRRs) were developed to predict the gas chromatographic (GC) relative retention times (RRTs) for 209 polybrominated diphenyl ether (PBDE) congeners using the heuristic method included in the computer software Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA). A total of 445 constitutional, topological, geometrical, electrostatic, and semi-empirical quantum chemical descriptors were derived for all PBDEs. Using experimental RRT data for 126 PBDE congeners from the literature, predictive regression models were built for seven individual GC capillary columns differing in stationary phases. Each model includes four descriptors which included Wiener index, Randic index, polarity parameter, etc., selected by CODESSA. High predictability was obtained. High multiple correlation coefficients R 2 indicated that >98.5% (except for stationary phase CP-Sil 19) of the total variation in the predicted RRTs is explained by the fitted models. The models were subsequently used to predict the RRTs of the remaining 83 PBDE congeners on seven different stationary phases. The statistical results show that, compared with others, DB-XLB column not only produces the least number of peak overlaps but also results in shorter retention times.

Quantitative Structure−Property Relationship (QSPR) Models for Boiling Points, Specific Gravities, and Refraction Indices of Hydrocarbons

The relationship between physical properties (normal boiling point, specific gravity, and refractive index) of hydrocarbons in the distillate boiling range and their molecular structures was examined using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. Multiple linear regression equations with up to eight parameters were used to develop predictive multilinear regression models. Six models for boiling point, specific gravity, and refractive index were obtained for the saturate and aromatic compounds separately. The correlation coefficients (R2) for all six models were >0.99, except for the model of specific gravity for aromatics, which had a correlation coefficient of R2 = 0.9881. The standard deviations over the 186-point saturate training set were 6.10 K, 0.007, and 0.004 for boiling point, specific gravity, and refractive index, respectively. Those over the 200-point aromatic training set were 6.30 K, 0.008, and 0.005, respectively. Leave-one-out cross-validatio...

A QSPR model for the prediction of the gas-phase free energies of activation of rotation around the NC(O) bond

A novel approach to predict the gas-phase rotational activation energies of amides is presented. The quantitative structure property relationship (QSPR) treatment, using the statistical package, comprehensive descriptors for structural and statistical analysis (CODESSA), resulted in a three-parameter equation with R 2=0.982 for the Δ G ≠ gas, of a set of 24 N, N-dialkylamides.

Relevance of theoretical molecular descriptors in quantitative structure–activity relationship analysis of α1-adrenergic receptor antagonists

A quantitative structure–activity relationship (QSAR) study of a wide series of structurally diverse α1-adrenergic receptor antagonists was performed using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) technique. Theoretical descriptors derived on a single structure and ad hoc defined size and shape descriptors were considered in the attempt of describing information relevant to receptor interaction. The relative effectiveness of these two classes of parameters in developing QSAR models for native (α1A and α1B) and cloned (α1a, α1b, and α1d) adrenergic receptor binding affinity, functional activity of vascular and lower urinary tract tissues, and in vitro and in vivo selectivity was evaluated.

Estimation of the Liquid Viscosity of Organic Compounds with a Quantitative Structure−Property Model

A QSPR (quantitative structure−property relationship) model for the estimation of the liquid viscosity of a large variety of organic compounds was established using the CODESSA (comprehensive descriptors for structural and statistical analysis) approach. The final model was developed with a calibration set containing 337 compounds. The multilinear regression (MLR) equation that relates ln η to five molecular descriptors has s = 0.37 and r = 0.920. The five theoretical parameters used in the QSPR model are molecular weight, Randic connectivity index of order 3, hydrogen-donor charged surface area HDCA-2 (electrostatic), maximum electrophilic reactivity index for a carbon atom, and maximum atomic orbital electronic population. The predictive ability of the MLR model was tested by the leave-20%-out cross-validation method, showing that the QSPR model is stable and can be used to obtain good predictions for compounds that were not used in the model calibration. The cross-validation statistical indices show a ...

QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications

An overview on the development of QSPR/QSAR equations using various descriptor-mining techniques and multilinear regression analysis in the framework of the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program is given. The description of the methodologies applied in CODESSA is followed by the presentation of the QSAR and QSPR models derived for eighteen molecular activities and properties. The properties cover single molecular species, interactions between different molecular species, properties of surfactants, complex properties and properties of polymers. A review with 54 references.

Quantitative structure-property relationship study of normal boiling points for halogen-/ oxygen-/ sulfur-containing organic compounds using the CODESSA program

QSPR (Quantitative Structure-Property Relationship) models for the estimation of boiling points of organic compounds containing halogens, oxygen, or sulfur without hydrogen bonding were established with the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program developed by Katritzky and coworkers. The boiling points of 185 compounds containing oxygen or sulfur can be accurately computed with a MLR (Multi-Linear Regression) equation containing six theoretical descriptors and having the following statistical indices: r = 0.992 and s = 6.3 °C. For a set of 534 halogenated alkanes C 1 – C 4 the best MLR equation with five descriptors has r = 0.990 and s = 9.0 °C. For subsets of molecules corresponding to monoethers, monosulfides, F- and Cl-containing compounds or F-containing compounds, even better MLR models were obtained, showing that a greater accuracy can be obtained for the estimation of boiling points with QSPR models developed for restricted classes of compounds. The QSPR models developed with CODESSA allow accurate computation of the boiling points of organic compounds using simple constitutional, topological, electrostatic and quantum indices that can be computed with standard quantum chemistry packages.

Relationships of Critical Temperatures to Calculated Molecular Properties

Quantitative structure−property relationships (QSPR) of critical temperatures with small numbers of physically significant molecular descriptors are developed using the CODESSA (comprehensive descriptors for structural and statistical analysis) technique. A highly significant one-parameter model correlates to the critical temperatures of 76 hydrocarbons at R2 = 0.953. A successful three-parameter model for 165 diverse compounds (R2 = 0.955) reveals fundamental structural influences on liquid-state properties.

Quantitative Structure−Property Relationship (QSPR) Correlation of Glass Transition Temperatures of High Molecular Weight Polymers

A new quantitative structure−property relationship (QSPR) five-parameter correlation (R2 = 0.946) of molar glass transition temperatures (Tg/M) for a diverse set of 88 polymers is developed with the Comprehensive Descriptors for Structural and Statistical Analysis (CODESSA) program. The descriptors are all calculated directly from the molecular structure, and the approach given is applicable, in principle, to all linear polymers of regular structure.

Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species.

The recognition of adverse effects due to environmental endocrine disruptors in humans and wildlife has focused attention on the need for predictive tools to select the most likely estrogenic chemicals from a very large number of chemicals for subsequent screening and/or testing for potential environmental toxicity. A three-dimensional quantitative structure-activity relationship (QSAR) model using comparative molecular field analysis (CoMFA) was constructed based on relative binding affinity (RBA) data from an estrogen receptor (ER) binding assay using calf uterine cytosol. The model demonstrated significant correlation of the calculated steric and electrostatic fields with RBA and yielded predictions that agreed well with experimental values over the entire range of RBA values. Analysis of the CoMFA three-dimensional contour plots revealed a consistent picture of the structural features that are largely responsible for the observed variations in RBA. Importantly, we established a correlation between the predicted RBA values for calf ER and their actual RBA values for human ER. These findings suggest a means to begin to construct a more comprehensive estrogen knowledge base by combining RBA assay data from multiple species in 3D-QSAR based predictive models, which could then be used to screen untested chemicals for their potential to bind to the ER. Another QSAR model was developed based on classical physicochemical descriptors generated using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. The predictive ability of the CoMFA model was superior to the corresponding CODESSA model.