GEometry, Topology, And Atom-Weights AssemblY Research Articles

QSPR models for predicting the Kovats retention indices of synthetic ester derivatives based on pyrethrin essential oil

ABSTRACT In this study, the chromatographic characteristics of 100 different pyrethroids including ester derivatives of cyclopropanecarboxylic acids were analyzed by measuring their logarithmic kovats retention index (log KRI) using a quantitative structure-retention relationship (QSRR). The log KRI of the studied pyrethroids were modeled by genetic algorithm-structure retention relationships (GA-QSRR) based on linear and nonlinear regression models. The descriptors such as HNar, H0v, and H5p, which express the GETAWAY (geometry, topology, and atom-weights assembly) compound descriptors, have a reasonable correlation with the log KRI. We assessed the predictive strength of the BP-ANN model and demonstrated the potential of the model using various statistical parameters. The statistical parameters such as Q2F1, Q2F2, Q2F3 , AAD, RMSE and CCC were used to evaluate the predictive ability of the BP-ANN model. In predicting the log KRI of pyrethroids, the results indicated that the BP-ANN model is more reliable and accurate than the BW-MLR model.

Investigation of the structural and physicochemical requirements ofquinoline-arylamidine hybrids for the growth inhibition of K562 and Rajileukemia cells

Quantitative structure-activity relationship (QSAR) analysis of 28 quinoline-arylamidine (CQArA) hybrids against two leukemia cells, K562 and Raji, was performed. Multiple linear regression (MLR) models were obtained by genetic algorithm. The best models involved the following descriptors: radial distribution function (RDF) descriptors, GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) descriptor, bond information content index, and dipole moment. The best MLR models for K562 and Raji cells demonstrated satisfactory stability in internal and external validation. Since the QSAR model for Raji cells has better predictive ability, two new highly potent CQArA analogues were proposed based on it. The QSAR models revealed important physicochemical and structural requirements for the antitumor activity: enhanced 3D molecular distribution of mass calculated at radius 11 Å from the center of molecule, a higher number of terminal electronegative atoms, extension of the molecules' central linker between quinoline and arylamidine, higher ratio of single bonds and total number of atoms, and symmetric charge distribution. Molecular docking study was applied to ensure the anticancer activity affinity to the binding site of the tyrosine-protein kinase (c-SRC). It was confirmed that the most active compound binds on the pocket between the small and large lobes of c-SRC, mostly throughout the hydrogen bonds and van der Waals interactions.

Which structural features stand behind micelization of ionic liquids? Quantitative Structure-Property Relationship studies

HypothesisDifferent ions constituting ionic liquids (ILs) change their properties, including the Critical Micelization Concentration (CMC). It is possible to identify and quantitatively describe specific structural ions’ features having influence on the micelization of ILs. Moreover, it should be possible to verify, whether the phenomenon of micelization is governed by the influence of the single ion only, rather than being a sum of both ions’ mutual influence. ExperimentalThe qualitative and quantitative description of the structural properties responsible for micelles formation was performed with the use of the Quantitative Structure-Property Relationship (QSPR) approach. Structural features were expressed with help of the molecular GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors system. The QSPR model was properly validated and its quality and usability was additionally proven by applying it to predict the CMC for 15,000 computationally designed ILs. It was the first model to the CMC assessment for ILs. FindingsThe analysis showed that longer (containing big hydrophobic domain), less spherical and not “folded” cations as well as bigger anions are the main factors causing the decrease of CMC. According to the presented model, the influence of cations and anions is independent.

A descriptor of amino acids SVWG and its applications in peptide QSAR

A descriptor, SVWG (principal component scores vector of WHIM (weighted holistic invariant molecular index) descriptors and GETWAY (GEometry, Topology, and Atom‐Weights AssemblY) descriptors), was derived from a principal component analysis of a matrix of two structural variables of coded amino acid. SVWG scales were then applied in three panels of peptide quantitative structure activity relationship (QSAR) that were molded by partial least squares regression. The obtained models with the correlation coefficient () and cross‐validation correlation coefficient () were 0.893 and 0.830 for angiotensin‐converting enzyme inhibitors, 0.990 and 0.968 for antimicrobial peptides, and 0.955 and 0.908 for oxytocin, respectively. Satisfactory results showed that information related to biological activity can be systemically expressed by SVWG scales, which may be a useful structural expression methodology for study on peptide QSAR. Copyright © 2012 John Wiley & Sons, Ltd.

Predicting Infinite Dilution Activity Coefficients of Chlorinated Organic Compounds in Aqueous Solution Based on Three-Dimensional WHIM and GETAWAY Descriptors

The ability of the Weighted Holistic Invariant Molecular (WHIM) and GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors to represent the effect of molecular structure on the infinite dilution activity coefficients (γ∞) of chlorinated organic compounds in aqueous solution was investigated. To this end, the dataset of 45 chlorinated organic compounds was randomly divided into a training set (35) and a test set (10). The genetic algorithm was employed for descriptor selection and model development. A five-parameter correlation equation for ln(γ∞) was obtained, with a squared correlation coefficient (R2) of 0.9609 and a standard error of estimation (s) of 0.599 for the training set. The reliability of the proposed model was further illustrated by means of the leave-one-out cross-validation procedure and validation through an external test set. All descriptors involved in the model can be derived directly from the structures of the compounds, which makes it very useful in predicting γ∞ of other chlorinated organic compounds not involved in the present dataset.

Adsorption of s-triazines onto polybenzimidazole: A quantitative structure–property relationship investigation

The adsorption of 25 symmetric triazines (s-triazines) on polybenzimidazole (PBI) beads is investigated under equilibrium (batch) conditions. The observed adsorption isotherms of the selected compounds are accurately described by the Freundlich model, while the agreement between the Langmuir model and the experimental data is moderately worse, which seems to reflect the heterogeneous meso- and micro-porosity of PBI and polydispersion in the interaction mechanism. Methylthio- and methoxytriazines exhibit a greater adsorption tendency as compared with chlorotriazines, moreover, progressive dealkylation of amino groups results in a progressive increase of triazine uptake on PBI. Based on these evidences, the adsorption mechanism seems to be governed by a combination of π–π and hydrogen-bonding interactions. Genetic algorithm (GA) variable selection and multilinear regression (MLR) are combined in order to describe the effect of triazine structure on the extraction performance of PBI according to the quantitative structure–property relationship (QSPR) method. q max, the amount of triazine adsorbed per weight unit of PBI assuming homogeneous monolayer (Langmuir) mechanism, exhibits a great variability within the set of investigated triazines and is the quantity here modelled by QSPR. On the other hand, the Freundlich constant, K F, which expresses the adsorption efficiency under multilayer heterogeneous conditions, even if markedly increases passing from chloro- to methylthio- or methoxytriazines, is less noticeably affected by the fine details of the adsorbate structure, as the number or nature of alkyl fragments bound to the amino groups. To quantitatively relate q max with the triazine structure GA-MLR analysis is performed on the set of 1664 theoretical molecular descriptors provided by the software Dragon. Finally, a four-dimensional QSPR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on four representative triazines excluded from model calibration. The four descriptors selected by GA-MLR, all belonging to the class of three-dimensional GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) descriptors, adequately represent the structural factors influencing the affinity of triazines to PBI in the batch extraction process.

QSAR modeling of anthocyanins, anthocyanidins and catechins as inhibitors of lipid peroxidation using three-dimensional descriptors

This work describes the quantiative structure–activity relationship (QSAR) study of lipid peroxidation inhibitory effect of catechins, anthocyanidins and anthocyanins using molecular descriptors and physicochemical parameters derived from optimised three-dimensional (3D) structure, since this set of studied compounds contains stereoisomers with different activities. Six groups of 3D descriptors have been used to generate QSAR models: geometrical, 3D molecule representation of structures based on electron diffraction (3D-MoRSE); Randic molecular profiles; geometry, topology and atom weights assembly (GETAWAY); radial distribution function (RDF); and weigthed covariance matrices (WHIM) descriptors. The 3D molecular descriptors and physicochemical parameters have been calculated applying the online software Parameter Client and HyperChem 8.0. The primary selection of 3D molecular descriptors and physicochemical parameters was based on their ability to discriminate stereoisomers. Further selection of predictor variables for multiple regression was performed by the best-subset and forward stepwise method. The best-developed QSAR models consisted of geometrical, RDF and Randic molecular profiles descriptors. Those descriptors could be used for the prediction of the biological activity of catechin stereoisomers and their derivates. The obtained models suggest that the inhibitory effect of studied compounds is related to the shape of the molecule and the three-dimensional distribution of atomic mass in the molecule.

Quantitative structure–retention relationships of pesticides in reversed-phase high-performance liquid chromatography based on WHIM and GETAWAY molecular descriptors

The ability of the Weighted Holistic Invariant Molecular (WHIM) and GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors to represent the effect of molecular structure on the retention of pesticides in reversed-phase high-performance liquid chromatography (RP-HPLC) is investigated. To this end, two retention data sets previously collected in water–acetonitrile mobile phase are re-examined. The first data set ( data-set-1) consists of retention data of 26 neutral compounds belonging to widely used pesticide classes, collected within the mobile phase composition range 40–65% (v/v) acetonitrile. The second data set ( data-set-2) describes retention of phenoxy acid herbicides and structurally related compounds (benzoic acid and phenylacetic acid derivatives), as a whole covering the p K a range 2.3–4.3, as a function of mobile phase composition, ranging between 30 and 70% (v/v) acetonitrile, and pH, ranging between 2 and 5. For each data set, the mobile phase attributes are combined with WHIM or GETAWAY descriptors into “mixed” predictive models in order to attempt retention modelling within the whole mobile phase composition range of analytical interest. Six- or seven-dimensional multilinear models, preliminarily selected using a genetic algorithm, were improved using a multi-layer artificial neural network (ANN) learned by back propagation. ANN performance was tested on three molecules not used in the learning stage and by leave-more-out cross validation. The results reveal that while WHIM descriptors seem not adequate to model retention of solutes of data-set-1, GETAWAY descriptors provide a satisfactory retention model. On the other hand WHIM and GETAWAY descriptors applied to data-set-2 provide a similar performance, even if slightly worse as compared with the above case. Accuracy of retention modelling in these cases is comparable or slightly poorer as compared with the results previously obtained by combining quantum chemical descriptors or usual physico-chemical solute properties (log k ow and p K a) and mobile phase attributes.

QSAR modeling of the rodent carcinogenicity of nitrocompounds

Chemical carcinogenicity is of primary interest, because it drives much of the current regulatory actions regarding new and existing chemicals, and its conventional experimental test takes around three years to design, conduct, and interpret as well as the costs of hundreds of millions of dollars, millions of skilled personnel hours, and several animal lives. Both academia and private companies are actively trying to develop alternative methods, such as QSAR models. This paper reports a QSAR study for predicting carcinogenic potency of nitrocompounds bioassayed in female rats. Several different theoretical molecular descriptors, calculated only on the basis of knowledge of the molecular structure and an efficient variable selection procedure, such as Genetic Algorithm, led to models with satisfactory predictive ability. But the best-final QSAR model is based on the GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors capturing a reasonable interpretation. In fact, structural features such as molecular shape-linear, branched, cyclic, and polycyclic--and bond length are some of the key factors flagging the carcinogenicity of this set of nitrocompounds. This QSAR model, after removal of one identified nitrocompound outlier, is able to explain around 86% of the variance in the experimental activity and manifest good predictive ability as indicated by the higher q(2)s of cross- and external-validations, which demonstrate the practical value of the final QSAR model for screening and priority testing. This model can be applied to nitrochemicals different from the studied nitrocompounds (even those not yet synthesized) as it is based on theoretical molecular descriptors that might be easily and rapidly calculated.

Investigation of retention behaviour of non-steroidal anti-inflammatory drugs in high-performance liquid chromatography by using quantitative structure–retention relationships

In this paper, a quantitative structure–retention relationship (QSRR) method is employed to model the retention behaviour in reversed-phase high-performance liquid chromatography of arylpropionic acid derivatives, largely used non-steroidal anti-inflammatory drugs (NSAIDs). Computed molecular descriptors and the organic modifier content in the mobile phase are associated into a comprehensive model to describe the effect of both solute structure and eluent composition on the isocratic retention of these drugs in water-acetonitrile mobile phases. Multilinear regression (MLR) combined with genetic algorithm (GA) variable selection is used to extract from a large set of computed 3D descriptors an optimal subset. Based on GA-MLR analysis, a five-dimensional QSRR model is identified. All the four selected molecular descriptors belong to the category of GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors. The related multilinear model exhibits a quite good fitting and predictive performance. This model is further improved using an artificial neural network (ANN) learned by error back-propagation. Finally, the ANN-based model displays a remarkably better performance as compared with the MLR counterpart and, based on external validation, is able to predict with good accuracy the behaviour of unknown arylpropionic NSAIDs in the range of mobile phase composition of analytical interest (between 35 and 75% acetonitrile (v/v)).

Quantitative structure–activity relationship studies of HIV-1 integrase inhibition. 1. GETAWAY descriptors

The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the HIV-1 integrase inhibition of 172 compounds that belong to 11 different chemistry families. A model able to describe more than 68.5% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the five different approaches, including the use of Randić Molecular Profiles, Geometrical, RDF, 3D-MORSE and WHIM descriptors was able to explain more than 62.4% of the variance in the mentioned property with the same number of variables in the equation. Finally, after extracting five compounds considered by us as outliers the model was able to describe more than 72.5% of the variance in the experimental activity.

Geometry, topology, and atom-weights assembly descriptors to predicting A1 adenosine receptors agonists

The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the A1 adenosine receptors agonist effect of 32 adenosine analogues: N6-arylcarbamoyl, 2-arylalkynyl-N6-arylcarbamoyl, and N6-carboxamido derivatives. A model, able to describe more than 77% of the variance in the experimental activity, was developed with the use of the above mentioned approach. Five different approaches (Topological, Galvez Topological Charges indexes, Randić Molecular Profiles, Geometrical, and WHIM descriptors) failed to give satisfactory models (R2=0.70) for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than GETAWAY, the best fitted out model was still found with these descriptors.

Kohonen Network Study of Aromatic Compounds Based on Electronic and Nonelectronic Structure Descriptors

Atoms in Molecules (AIM) and Electron Localization Function (ELF) methodologies were applied to describe the electronic structure of 88 aromatic compounds. The analyzed database contains molecules substituted by nucleophilic and electrophilic groups which are responsible for electron density distribution in the molecule and further for its reactivity. Radial Distribution Function (RDF), Weighted Holistic Invariant Molecular (WHIM), Three-Dimensional Molecule Representation of Structures based on Electron Diffraction (3D-MoRSE) and Geometry, Topology and Atom-Weights Assembly (GETAWAY) descriptors were taken into account describing the structures of the analyzed molecules. According to generated descriptor space the classification of the molecules has been subsequently performed using unsupervised learning strategy and Kohonen network. The final step of descriptor space testing was supervised learning of Counter-Propagation Artificial Neural Network (CPANN) using n-octanol/water partition coefficient (logP), dipole moment (DM) and molecular refractivity (MR) as target values.

Quantitative structure–retention relationships XIV: Prediction of gas chromatographic retention indices for saturated O-, N-, and S-heterocyclic compounds

In this study, quantitative structure–retention relationship (QSRR) technique was used to find the best approximation and to predict gas chromatographic retention indices for O-, N-, and S-heterocyclic compounds on standard nonpolar polydimethyl siloxane stationary phase. Boiling point (BP) and calculated properties were used to encode the structure of compounds. Three- and two-dimensional calculated properties such as weighted–holistic invariant molecular (WHIM) descriptors, geometry topology and atom weights assembly (GETAWAY) descriptors, connectivity indices, and zero-dimensional constitutive descriptors were used. Variable subset selection (VSS) and partial least squares (PLS) projections to latent structures were used to select the most significant variables from a large set of descriptors. Multiple linear regression (MLR) and PLS were applied to find the relationship between selected properties and gas chromatographic retention indices. PLS was not able to select the most important descriptors (boiling point or molecular weight). The predictive ability of the models was tested by cross-validation. Solely calculated descriptors were not able to give proper models. Boiling point was always necessary for good prediction. PLS models containing boiling points were suitable for retention index prediction, whereas MLR did not give real linear models.

Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors.

Novel molecular descriptors based on a leverage matrix similar to that defined in statistics and usually used for regression diagnostics are presented. This leverage matrix, called Molecular Influence Matrix (MIM), is here proposed as a new molecular representation easily calculated from the spatial coordinates of the molecule atoms in a chosen conformation. The proposed molecular descriptors are called GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) as they try to match 3D-molecular geometry provided by the molecular influence matrix and atom relatedness by molecular topology, with chemical information by using different atomic weightings (atomic mass, polarizability, van der Waals volume, and electronegativity, together with unit weights). A first set of molecular descriptors, called H-GETAWAY, is derived by using only the information provided by the molecular influence matrix, while a second set, called R-GETAWAY, combines this information with geometric interatomic distances in the molecule. The prediction ability in structure-property correlations of the new descriptors was tested by analyzing regressions of these descriptors for selected properties of octanes.

Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies.

In a previous paper the theory of the new molecular descriptors called GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) was explained. These descriptors have been proposed with the aim of matching 3D-molecular geometry, atom relatedness, and chemical information. In this paper prediction ability in structure-property correlations of GETAWAY descriptors has been tested extensively by analyzing the regressions of these descriptors for selected properties of some reference compound classes. Moreover, the general performance of the new descriptors in QSAR/QSPR has been evaluated with respect to other well-known sets of molecular descriptors.