Three-dimensional quantitative structure-activity relationship study for phenylsulfonyl carboxylates using CoMFA and CoMSIA

From both the comparative molecular field analysis (CoMFA) and the comparative molecular similarity indices analysis (CoMSIA), the paper describes two three-dimensional quantitative structure-activity relationship (3D-QSAR) models for the acute toxicity logEC50 (15 min-EC50 in micromoll(-1)) of 56 phenylsulfonyl carboxylates on Photobacterium phosphoreum. Two models yield the leave-one-out cross-validated correlation coefficient q2 values of 0.823 and 0.713, and the conventional correlation coefficient r2 values of 0.958 and 0.933, respectively. The achievement of higher q2 and r2 values of CoMFA model indicates the significance of correlation of steric and electrostatic fields with biological activities. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight into the toxic mechanism of tested compounds. The quality of CoMSIA model is slightly lower than that of CoMFA in terms of q2 and r2 values. Not requiring molecular superposition, CoMSIA is faster than CoMFA in data processing.     

Studies of 3D-quantitative structure-activity relationships on a set of nitroaromatic compounds: CoMFA,advanced CoMFA and CoMSIA

By using comparative molecular field analysis (CoMFA), advanced CoMFA and comparative molecular similarity index analysis (CoMSIA) methods, the 3D relationships between the structures of 35 nitroaromatic compounds and their toxicities have been investigated to yield statistically reliable models of considerable predictive power. In contrast to CoMFA, CoMSIA produces better results for the correlation. Moreover, the obtained CoMSIA contour maps that interpret the correlations in terms of field contributions allow physicochemical properties relevant for binding to be easily mapped back onto molecular structures, and thus elucidate structural features among ligands that are responsible for toxicities. Besides, most of the highlighted regions in CoMSIA and CoMFA contour maps are mirrored by features in the surrounding environment. Thereby, CoMFA and CoMSIA both help to give explanations of the toxic mechanism of tested compounds.     

3D-QSAR of fungal quorum-sensing inhibiting analogs of farnesol

Thirty-four analogs with variable antifungal activity were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analyses (CoMSIA) were conducted on the group of analogs to determine the structural requirements for selectivity and potency in inhibiting biofilm formation and fungal growth. The best CoMFA model predicted a q(2) = 0.5 and an r(2) = 0.991, and revealed that electrostatic properties play a significant role in potency and selectivity. The best CoMSIA model combined electrostatics, hydrogen bond acceptor and donor, and hydrophobic fields with a q(2) = 0.664 r(2) = 0.952, S = 0.099, and F = 139.892. The analyses of the contour maps from both models provide significant insight into the structural necessities for a potent compound. Therefore, manipulating various chemical properties of the substituted groups on the farnesol chain can be used to enhance the fungicidal properties of the target compound.     

Three-dimensional, quantitative-structure-property-relationship study of aqueous solubility for phenylsulfonyl carboxylates using comparative-molecular-field analysis and comparative-molecular-similarity-indices analysis.

With both the comparative-molecular-field analysis (CoMFA) and the comparative-molecular-similarity-indices analysis (CoMSIA), the paper describes two five-component, three-dimensional, quantitative-structure-property-relationship (3D-QSPR) models for the aqueous solubility logSw (Sw, mol x L(-1)) of 52 phenylsulfonyl carboxylates. Two models yield the leave-one-out cross-validated correlation coefficient q2 values 0.851 and 0.821, and the conventional correlation coefficient r2 values 0.963 and 0.929, respectively. The achievement of high q2 and r2 values of the CoMFA model indicates the significance of correlation of steric and electrostatic fields with the aqueous solubility. The key features in the CoMFA contour maps are critical to trace the important properties and gain insight to the solvation mechanism of tested compounds. The quality of CoMSIA model is slightly lower than that of CoMFA in terms of q2 and r2 values. Not requiring molecular superposition, CoMSIA may be faster than CoMFA in data processing.     

Three-dimensional quantitative structure activity relationships of quorum-sensing and biofilm inhibitors in gram-negative bacteria

Thirty N-acyl homoserine lactone (AHL) analogs with variable antibacterial activity and displaying inhibition of biofilm formation were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) were carried out to determine the optimum structural requirements for selectivity and potency of quorum-sensing and bacterial biofilm inhibition. The best CoMFA model predicted a q2 value of 0.519 and an r2 value of 0.984 and revealed that electrostatic and steric properties play a significant role in potency and selectivity. The CoMSIA model predicted a q2 value of 0.411 and an r2 value of 0.938 based on a combination of steric, electrostatic, and hydrophobic effects. The analysis of the contour maps from each model provide insight into the structural requirements for increasing the activity of a compound. Consequently, manipulating the chemical and physical properties of substituted acyl groups on the homoserine lactone moiety can provide important information toward enhancing the antibacterial properties of the target chemical compound.     

Three-dimensional quantitative structure activity relationship (3D-QSAR) analysis for in vitro toxicity of chlorophenols to HepG2 cells

In the present paper, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to investigate two 3D-QSAR models for the cytotoxicity of chlorophenols. These models have evaluated the intensity of chlorophenols' toxicity on HepG2 cells in vitro. The CoMFA model has both high consistency and predictability. The contribution of the electrostatic field to biological activity is greater than that of the steric field. The CoMSIA model used in this study includes two fields, one is hydrophobic field, and the other is electrostatic field. The relative contribution of them is 0.789:0.211. Consisted with the CoMFA model, the CoMSIA electrostatic filed also plays a dominant role. The CoMFA and CoMSIA contour maps significantly elucidated that the electrostatic field is more important than the other fields and might be one of the reasons resulting in potential reactive mechanism involved in cell proliferation inhibition.     

3D-QSAR of fungal quorum-sensing inhibiting analogs of farnesol

Thirty-four analogs with variable antifungal activity were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analyses (CoMSIA) were conducted on the group of analogs to determine the structural requirements for selectivity and potency in inhibiting biofilm formation and fungal growth. The best CoMFA model predicted a q² = 0.5 and an r² = 0.991, and revealed that electrostatic properties play a significant role in potency and selectivity. The best CoMSIA model combined electrostatics, hydrogen bond acceptor and donor, and hydrophobic fields with a q² = 0.664 r² = 0.952, S = 0.099, and F = 139.892. The analyses of the contour maps from both models provide significant insight into the structural necessities for a potent compound. Therefore, manipulating various chemical properties of the substituted groups on the farnesol chain can be used to enhance the fungicidal properties of the target compound.     

Insights into the structure of urea-like compounds as inhibitors of the juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta: analysis of the binding modes and structure-activity relationships of the inhibitors by docking and CoMFA calculations

Substituted urea compounds are well-known as potent inhibitors of juvenile hormone epoxide hydrolase (JHEH) of the tobacco hornworm Manduca sexta. Docking simulations of 47 derivatives inside JHEH were performed to gain insight into the structural characteristics of these complexes. The obtained orientations show a strong similitude with the observed in the known X-ray crystal structures of human soluble epoxide hydrolase (sEH) complexed with dialkylurea inhibitors. In addition, the predicted inhibitor concentration (IC₅₀) of the above-mentioned compounds as JHEH inhibitors were obtained by a quantitative structure-activity relationship (QSAR) method by using comparative molecular field analysis (CoMFA) applied to aligned dataset. The best models included steric and electrostatic fields and had adequate predictive abilities. In addition, these models were used to predict the activity of an external test set of compounds that was not used for building the model. Furthermore, plots of the CoMFA fields allowed conclusions to be drawn for the choice of suitable inhibitors.     

Investigation of structural requirements for inhibitory activity at the rat and housefly picrotoxinin binding sites in ionotropic GABA receptors using DISCOtech and CoMFA

A number of widely diverse compounds that show inhibitory activities at the picrotoxinin binding sites in housefly and rat GABA receptors were investigated by using the distance comparison technique (DISCOtech) and comparative molecular field analysis (CoMFA) methods to explore the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds. These compounds consist of three diverse types of noncompetitive GABA receptor antagonists, i.e., trioxabicyclooctanes and their derivatives, picrodendrins and related terpenoids, and fipronil and its analogs. For investigation of the structural requirements for inhibitory activity at the picrotoxinin binding site of GABA receptor, DISCOtech pharmacophore models containing one center of hydrophobic ring and two hydrogen bond acceptor atoms for both housefly-head and rat-brain GABA receptors were constructed, respectively. In particular, the interacting areas in housefly receptors appear to be wider than that in rat receptors, the differences between rat and housefly receptor models implicate the selectivity of noncompetitive GABA receptor antagonists. In addition, corresponding CoMFA models with good statistical indices (r(2)>0.9 and q(2)>0.5) were also obtained. These models can be used as guidance for the development of new compounds with high activities and selectivities.     

HQSAR and CoMFA approaches in predicting reactivity of halogenated compounds with hydroxyl radicals

Two quantitative structure–activity relationship (QSAR) methods: hologram QSAR (HQSAR) and comparative molecular field analysis (CoMFa) were evaluated for predicting half-lives of the hydroxyl radicals reaction with substituted aromatic compounds. The HQSAR approach, which is topological in nature, results in a mathematical model which was more stable and has a greater predictive ability than the model derived on the 3-D CoMFA approach. Interpretations of the colour coded results of both methods are in good agreement with the proposed mechanism of the hydroxyl radical oxidation of halogenated aromatic compounds in the atmosphere.     

Three-dimensional quantitative structure activity relationships of quorum-sensing and biofilm inhibitors in gram-negative bacteria

Thirty N-acyl homoserine lactone (AHL) analogs with variable antibacterial activity and displaying inhibition of biofilm formation were selected to develop models for establishing three-dimensional quantitative structure-activity relationships (3D-QSAR). Comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) were carried out to determine the optimum structural requirements for selectivity and potency of quorum-sensing and bacterial biofilm inhibition. The best CoMFA model predicted a q² value of 0.519 and an r² value of 0.984 and revealed that electrostatic and steric properties play a significant role in potency and selectivity. The CoMSIA model predicted a q² value of 0.411 and an r² value of 0.938 based on a combination of steric, electrostatic, and hydrophobic effects. The analysis of the contour maps from each model provide insight into the structural requirements for increasing the activity of a compound. Consequently, manipulating the chemical and physical properties of substituted acyl groups on the homoserine lactone moiety can provide important information toward enhancing the antibacterial properties of the target chemical compound.     

Mechanism-based quantitative structure-activity relationships for the inhibition of substituted phenols on germination rate of Cucumis sativus

Comparative inhibition activity (GC50) of 42 structurally diverse substituted phenols on seed germination rate of Cucumis sativus was investigated. Quantitative structure-activity relationships (QSARs) were developed by using hydrophobicity (1-octanol/water partition coefficient, logKow) and electrophilicity (the energy of the lowest unoccupied molecule orbital, Eluma) for the toxicity of phenols according to their modes of toxic action. Most phenols elicited their response via a polar narcotic mechanism and a highly significant log Kow-based model was obtained (GC50 = 0.92 log Kow + 1.99, r2 0.84, n = 29). The inclusion of E(lumo) greatly improved the predictive power of the polar narcotic QSAR (GC50 = 0.88 log Kow - 0.30E(lumo) + 1.99, r2 = 0.93, n = 29). pKa proved to be an insignificant influencing factor in this study. Poor correlation with hydrophobicity and strong correlation with electrophilicity were observed for the nine bio-reactive chemicals. Their elevated toxicity was considerably underestimated by the polar narcotic logKow-dependent QSAR. The nine chemicals consist of selected nitro-substituted phenols, hydroquinone, catechol and 2-aminophenol. Their excess toxic potency could be explained by their molecular structure involving in vivo reaction with bio-macromolecules. Strong dissociation of carboxyl group of the four benzoic acid derivatives greatly decreased their observed toxicity. In an effort to model all chemicals including polar narcotics and bio-reactive chemicals, a response-surface analysis with the toxicity, logKow and E(lumo) was performed. This resulted in a highly predictive two-parameter QSAR for most of the chemicals (GC50 = 0. 70 logKow - 0.66E(lumo) + 2.17, r2 = 0.89, n = 36). Catechol and 2,4-dinitrophenol proved to be outliers of this model and their much high toxicity was explained.     

Quantitative structure-activity relationships for the inhibition toxicity to root elongation of Cucumis sativus of selected phenols and interspecies correlation with Tetrahymena pyriformis

The comparative toxicities of selected phenols to higher plants Cucumis sativus were measured and the negative logarithm molar concentration of the root elongation median inhibition (IRC50) were derived. Quantitative structure-activity relationships (QSARs) were developed to explore the toxicity influencing factors and for predictive purpose. The toxicity data, fell into two classes: polar narcosis and bio-reactive. For polar narcotic phenols, a highly significant two-parameter QSAR based on 1-octanol/water partition coefficient (logKow) and energy of the lowest unoccupied orbital (E(lumo)) was derived (IRC50 = 0.77 log Kow - 0.39E(lumo) + 2.36 n = 22 r2 = 0.89). The five bio-reactive chemicals proved to show elevated toxicity due to their typical substructure involved diverse reactive mechanisms. In an effort to model all chemicals, a robust multiple-variable QSAR combining logKow, E(lumo) and Qmax, the most negative net atomic charge, was developed (IRC50 = 0.65 logKow - 0.72E(lumo) + 0.23Qmax + 2.81 n = 27 r2 = 0.94), indicating that hydrophobicity, electrophilicity and hydrogen bond interaction contribute mainly to the phytotoxicity. The toxicological data was compared with Tetrahymena pyriformis 2-d population growth inhibition toxicity (IGC50) and excellent interspecies correlations were observed both for the polar narcotics and for five reactive chemicals (for polar narcotics: IRC50 = 0.95IGC50 + 1.07 n = 16 r2 = 0.89; for bio-reactive chemicals: IRC50 = 0.98IGC50 + 2.19 n = 5 r2 = 0.97; and for all: IRC50 = 0.93IGC50 + 1.63 n = 21 r2 = 0.87). This suggested that T pyriformis toxicity could serve as a surrogate of C. sativus toxicity for phenols and interspecies correlation also could be established for reactive chemicals.     

Acute toxicity of benzene derivatives to the tadpoles (Rana japonica) and QSAR analyses

Acute lethal toxicity (the negative logarithm of molar concentrations of 12 h acute median lethal, expressed as 12 h-log1/LC50) of 46 benzene derivatives to Rana japonica tadpoles was determined. 1-octanol/water partition coefficient (logKow)-dependent models were developed to study the toxicity of different categories chemicals. In an effort to model all chemicals, response surface analyses and stepwise multiple regression analyses were performed and successful models were obtained. A general and robust QSAR model was achieved with the combined application of variables reflecting hydrophobicity, electric property, and molecular size respectively (12h-log1/LC50 = 0.393logKow - 0.428Elumo + 0.0110Vol. + 1.362 n = 51, r2 = 0.834) using stepwise multiple regression analyses. Because of strong dissociation of carboxyl group greatly decreasing their observed toxicity, using logDow in instead of logKow the quality of the models is greatly improved. The conventional r2 and cross-validation r2(CV) were 0.914 and 0.785, respectively, indicating that QSAR was both internally consistent and highly predictive.     

A novel approach to predict aquatic toxicity from molecular structure

The main aim of the study was to develop quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity using atom-based non-stochastic and stochastic linear indices. The used dataset consist of 392 benzene derivatives, separated into training and test sets, for which toxicity data to the ciliate Tetrahymena pyriformis were available. Using multiple linear regression, two statistically significant QSAR models were obtained with non-stochastic (R2=0.791 and s=0.344) and stochastic (R2=0.799 and s=0.343) linear indices. A leave-one-out (LOO) cross-validation procedure was carried out achieving values of q2=0.781 (scv=0.348) and q2=0.786 (scv=0.350), respectively. In addition, a validation through an external test set was performed, which yields significant values of Rpred2 of 0.762 and 0.797. A brief study of the influence of the statistical outliers in QSAR's model development was also carried out. Finally, our method was compared with other approaches implemented in the Dragon software achieving better results. The non-stochastic and stochastic linear indices appear to provide an interesting alternative to costly and time-consuming experiments for determining toxicity.     

QSAR trout toxicity models on aromatic pesticides

The pesticides originally designed to kill target organisms are dangerous for many other wild species. Since they are applied directly to the environment, they can easily reach the water basins and the topsoil. A dataset of 125 aromatic pesticides with well-expressed aquatic toxicity towards trout was subjected to quantitative structure activity relationships (QSAR) analysis aimed to establish the relationship between their molecular structure and biological activity. A literature data for LC₅₀ concentration killing 50% of fish was used. In addition to the standard 2D-QSAR analysis, a comparative molecular field analysis (CoMFA) analysis considering the electrostatic and steric properties of the molecules was also performed. The CoMFA analysis helped the recognition of the steric interactions as playing an important role for aquatic toxicity. In addition, the transport properties and the stability of the compounds studied were also identified as important for their biological activity.     

QSAR trout toxicity models on aromatic pesticides

The pesticides originally designed to kill target organisms are dangerous for many other wild species. Since they are applied directly to the environment, they can easily reach the water basins and the topsoil. A dataset of 125 aromatic pesticides with well-expressed aquatic toxicity towards trout was subjected to quantitative structure activity relationships (QSAR) analysis aimed to establish the relationship between their molecular structure and biological activity. A literature data for LC50 concentration killing 50% of fish was used. In addition to the standard 2D-QSAR analysis, a comparative molecular field analysis (CoMFA) analysis considering the electrostatic and steric properties of the molecules was also performed. The CoMFA analysis helped the recognition of the steric interactions as playing an important role for aquatic toxicity. In addition, the transport properties and the stability of the compounds studied were also identified as important for their biological activity.     

Correlating metal ionic characteristics with biosorption capacity using QSAR model

The relationship between metal ionic characteristics and the maximum biosorption capacity (q(max)) was established using QSAR model based on the classification of metal ions (soft, hard and borderline ions). Ten kinds of metal ions (Ag(+), Cs(+), Zn(2+), Pb(2+), N(i2+), Cu(2+), Co(2+), Sr(2+), Cd(2+), Cr(3+)) were selected and the waste biomass of Saccharomyces cerevisiae obtained from a local brewery was used as biosorbent. Eighteen parameters of physiochemical characteristics of metal ions were selected and correlated with q(max). Classification of metal ions could improve the QSAR models and different characteristics were significant in correlating with q(max), such as polarizing power Z(2)/r or the first hydrolysis constant |logK(OH)| or ionization potential IP. X(m)(2)r seemed to be suitable for metal ions including soft ions, and Z(2)/r, |logK(OH)| and IP suitable for only soft ions or metal ions excluding soft ions. It provided a new way to predict the biosorptive capacity of metal ions.     

The effect of nitroaromatics' composition on their toxicity in vivo: novel, efficient non-additive 1D QSAR analysis

Novel 1D QSAR approach that allows analysis of non-additive effects of molecular fragments on toxicity has been proposed. Twenty-eight nitroaromatic compounds including some well-known explosives have been chosen for this study. The 50% lethal dose concentration for rats (LD50) was used as the estimation of toxicity in vivo to develop 1D QSAR models on the framework of Simplex representation of molecular structure. The results of 1D QSAR analysis show that even the information about the composition of molecules provides the main trends of toxicity changes. The necessity of consideration of substituents' mutual impacts for the development of adequate QSAR models of nitroaromatics' toxicity was demonstrated. Statistic characteristics for all the developed partial least squares QSAR models, except the additive ones are quite satisfactory (R2=0.81-0.92; Q2=0.64-0.83; R2 test=0.84-0.87). A successful performance of such models is due to their non-additivity i.e. possibility of taking into account the mutual influence of substituents in benzene ring which plays the governing role for toxicity change and could be mediated through the different C-H fragments of the ring. The correspondence between observed and predicted by these models toxicity values is good. This allowing combine advantages of such approaches and develop adequate consensus model that can be used as a toxicity virtual screening tool.