Molecular Electronegativity Distance Vector Research Articles

Study on the characterization of pesticide modes of action similarity and the multi-endpoint combined toxicity of pesticide mixtures to Caenorhabditis elegans

With the widespread use of pesticides, the coexistence of multiple low-residue pesticides in environmental media has increased significantly, and the “cocktail” effect caused by this phenomenon has garnered increasing attention. However, owing to the scarcity of information regarding the modes of action (MOAs) of chemicals, the application of concentration addition (CA) models for evaluating and predicting the toxicity of mixture with similar MOAs is limited. Additionally, the joint toxicity laws of complex mixture systems to different toxicity endpoints in organisms remain unclear, and effective methods to test the mixture toxicity on lifespan and reproductive inhibition are lacking. Therefore, in this study, the similarity of pesticide MOAs was characterized using molecular electronegativity-distance vector (MEDV-13) descriptors based on eight pesticides (aldicarb, methomyl, imidacloprid, thiamethoxam, dichlorvos, dimethoate, methamidophos and triazophos). Additionally, the methods of lifespan and reproduction inhibition microplate toxicity analysis of elegans (EL-MTA and ER-MTA) were established to test the lifespan and reproduction inhibition toxicity of Caenorhabditis elegans. Finally, a unified scale synergistic-antagonistic heatmap (SAHscale) method was proposed to explore the combined toxicity of the mixtures on the lifespan, reproduction, and mortality of nematodes. The results showed that the MEDV-13 descriptors could effectively characterize the similarity in MOAs. The lifespan and reproductive ability of Caenorhabditis elegans were significantly inhibited when the pesticide exposure concentration was one order of magnitude lower than the lethal dose. The sensitivity of lifespan and reproductive endpoints to mixtures was dependent on the concentration ratio. The same rays in the mixture had consistent toxicity interactions on the lifespan and reproductive endpoints of Caenorhabditis elegans. In conclusion, we demonstrated the feasibility of MEDV-13 in characterizing the similarity of MOAs, and provided a theoretical basis for exploring the mechanism of chemical mixtures by studying their apparent toxicity of mixtures on nematode lifespan and reproduction endpoints.

Study on anti-fighting activity of mice by artificial neural network

In order to study the quantitative structure-activity relationship (QSAR) of the anti-fighting activity E for 30 benzodiazepinooxazole derivatives against male mice, the molecular electronegativity distance vector M_D (D=1, 2, …, 91) of these compounds is calculated according to molecular topological environment. The three-variable (M₅₉, M₁₆, M₁₀) QSAR model of E for the compounds is constructed by stepwise regression method. The result demonstrates that the model is robust and has good prediction ability under <italic>R</italic>²_cv, <italic>V</italic>_IF tests. The M₅₉, M₁₆, M₁₀ are used as the input neurons of artificial neural network (ANN), and a 3∶5∶1 network architecture is employed. A satisfying BP-<italic>E</italic> model is constructed with the back-propagation algorithm, with the correlation coefficient R² and the standard error S being 0.984 and 0.054, respectively, showing that the relationship between E and the three structural parameters has a good nonlinear correlation. According to the three parameters of the model, it is clear that the dominant factors that impact the anti-fighting activity of benzodiazepineoxazole derivatives on male mice are the microscopic fragments: —CH₃, —CH₂—, , —NH— and —OH (O).

Study on the Balance Activity of Mice Based on BP-ANN

In order to study the quantitative structure-activity relationship (QSAR) of the balance activity (pH) for 30 benzodiazepinooxazole derivatives to male mice, the molecular electronegativity distance vector (M D) of above compounds was calculated by program according to molecular topological environment in this paper. The five-variable (M 6, M 46, M 25, M 63, M 70) QSAR model of pH for above compounds was constructed by using leaps-and-bounds regression method. The result demonstrates that the model is robustness and good prediction ability by using R cv 2, F tests. The M 6, M 46, M 25, M 63 and M 70 were used as the input neurons of artificial neural network (ANN), and a 5:3:1 network architecture was employed. A satisfying BP-pH model could be constructed with the back-propagation algorithm, with the correlation coefficient (R 2) and the standard error(S D) being 0.928 and 0.117, respectively, showing that the relationship between pH and these structural parameters has a good nonlinear correlation. Form the three parameters of the model, it is known that the dominant influence factors of increased balance activity are the microscopic fragments: –CH3, –CH2–, >C<, –NH–, –N< and –X in the molecules.

QSAR Study on the anti-tumor activity of levofloxacin-thiadiazole HDACi conjugates

A molecular electronegativity distance vector(Mt) based on 13atomic types is used to describe the structures of 19 conjugates(LHCc) of levofloxacin-thiadiazole HDAC inhibitor(HDACi) and related to the anti-tumor activity (MF and PC) of LHCc against MCF-7 and PC-3. The quantitative structure-activity relationships (QSAR) was established by using leaps-and-bounds regression analysis for the anti-tumor activities (MF and PC) of 19 above compounds to MCF-7and PC-3 along with the Mt. The correlation coefficients (R2) and the leave-one-out (LOO) cross validation Rcv2 for the MF and PC models were 0.792 and 0.679; 0.773 and 0.565, respectively. The QSAR models have favorable correlation, as well as robustness and good prediction capability by R2, F, Rcv2, AICFITVIF tests. The results indicate that the molecular structural units: －CHg－(g=1, 2), －NH2, －NH－,－OH, O=, －O－, －S－ and －X are main factors which can affect the anti-tumor activity MF and PC bioactivities of these compounds directly.

Ratio of cord to maternal serum PCB concentrations in relation to their congener-specific physicochemical properties.

The aim was to characterize placental transfer of some congeners of polychlorinated biphenyls (PCBs) and to relate human in utero exposure to these pollutants to their physicochemical properties. We included into the study 1134 births during the period 2002-2003 from two highly PCB contaminated districts in eastern Slovakia. Concentrations of 15 PCB congeners (IUPAC No. 28, 52, 101, 123(+149), 118, 114, 153, 105, 138(+163), 167, 156(+171), 157, 180, 170, and 189) in umbilical cord (C) and maternal serum (M) were determined. The C/M ratios were significantly related, either positively or inversely depending on parameter, to the logarithm of partition coefficient octanol-water (KOW), to fusion enthalpy at the melting point, molecular weight, water solubility, total surface area of the molecule, solvent accessible surface area, melting point, molar volume, and molecular electronegativity distance vector. We found an inverse association between logKOW and lipid adjusted logC/M (const=1.078, b1=-0.179, p<0.001, R(2)=0.039). Parameters evaluated were interrelated except fusion enthalpy at the melting point and electron affinity vs. solubility. We discuss the possible role of cholesterol as a transplacental transporter of PCBs.

Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

Prediction of blood–brain partitioning: A model based on molecular electronegativity distance vector descriptors

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood–brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient ( r), leave-one-out (LOO) cross-validation correlation coefficient ( q), and predictive correlation coefficient ( R p ). It has been found that PLSR model has good quality, r = 0.9202, q = 0.7956, and R p = 0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as –CH 3, –CH 2–, CH–, C , C–, –CH<, C<, N–, –NH–, O, and –OH, are the most important factors affecting the BBB permeability.

Structural characterization and Kovats retention index prediction for oxygen-containing organic compounds

A method of molecular structural characterization (MSC) called hydrogen-association classified molecular electronegativity-distance vector (H-MEDV) was used to describe the molecular structure of 106 oxygen-containing organic compounds. The samples were divided into a training set and a test set. For the training set, a QSRR model was built up by using multiple linear regression (MLR) and this model was evaluated by performing the cross-validation with the leave-one-out (LOO) procedure. The correlation coefficient ( R) and the cross-verification correlation coefficient ( R CV) of the model were 0.964 and 0.915, respectively. Furthermore, the validity of the model was tested by the test set and satisfied results with correlation coefficient ( R est) of 0.948. The results show that the model constructed in this work could provide estimation stability and favorable predictive ability.

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.

A Mathematical Model between Bioconcentration Factors and Topological Indices of Organic Pollutants

The six-parameter QSFR(quantitative structure-bioconcentration factor relationship) model of the bioconcentration factor (FBC) was constructed based on valence connectivity index (xi), electrotopological state index (ej), and molecular electronegativity distance vector (mk), for 239 organic pollutants. The traditional correlation coefficient (R2) and the cross-validation correlation coefficient (Q2) were 0.821 and 0.809, respectively. The result demonstrated that the model was highly reliable and had good predictive ability from the point of view of statistics. From the six parameters of the model, it was known that the dominant influencing factors of bioconcentration factor were the molecular structure fragments: —C—, , —O—, —X, —NH2, and the space factors: the flexibility and the puckered degree of organic pollutant molecules. It may be considered that the induced fit mechanism plays a definite action in the process of biological enrichment. The orthogonal transformation of independent variable did not influence the correlativity of model and could reduce the autocorrelation of independent variable.

Molecular electronegativity distance vector model for the Prediction of bioconcentration factors in fish

Molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was used to describe the structures of 122 nonionic organic compounds (NOCs) and a quantitative relationship between the MEDV descriptors and the bioconcentration factors (BCF) of NOCs in fish was developed using the variable selection and modeling based on prediction (VSMP). It was found that some main structural factors influencing the BCFs of NOCs are the substructures expressed by four atomic types of nos. 2, 3, 5, and 13, i.e., atom groups -CH(2)- or =CH-, -CH< or =C<, -NH(2), and -Cl or -Br where the former two groups exist in the molecular skeleton of NOC and the latter three groups are related closely to the substituting groups on a benzene ring. The best 5-variable model, with the correlation coefficient (r(2)) of 0.9500 and the leave-one-out cross-validation correlation coefficient (q(2)) of 0.9428, was built by multiple linear regressions, which shows a good estimation ability and stability. A predictive power for the external samples was tested by the model from the training set of 80 NOCs and the predictive correlation coefficient (u(2)) for the 42 external samples in the test set was 0.9028.

Estimation and prediction on retention times of components from essential oil of Paulownia tomentosa flowers by molecular electronegativity-distance vector (MEDV)

The molecular electronegativity-distance vector (MEDV) descriptors are used to describe the structures of 69 compounds isolated from the essential oil of Paulownia tomentosa flowers. Through multiple linear regression (MLR), the correlation coefficient 0.964 is achieved. We select five and six vectors to build the models through the method of stepwise multiple regression (SMR), and the correlation coefficients of the two models are 0.962 and 0.964. Then all the models are evaluated by performing the cross-validation with the leave-one-out (LOO) procedure and the satisfactory results with correlation coefficients of 0.941, 0.954 and 0.954, respectively. So we can get the conclusion that the stability and predictability of the model is good.

Predicting bioconcentration factor values of organic pollutants based on MEDV descriptors derived QSARs

The molecular electronegativity-distance vector (MEDV) is employed to describe the chemical structure of organic pollutants. Quantitative linear relationships between the molecular descriptors and BCF values are developed by best subset regression and partial least square regression analysis. The main structural factors influencing the bioactivities are -CH2, -X, -C≮, -C≮, -O-. The high values of r2 and q2LOO present good estimation ability and stability of models. The prediction power for external samples is validated by the model developed from the training set.

A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV)

Polychlorinated biphenyls (PCBs) are some of the most prevalent pollutants in the total environment and receive more and more concerns as a group of ubiquitous potential persistent organic pollutants. Using the variable selection and modeling based on prediction (VSMP), the molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was employed to develop a linear model (MI) between the bioconcentration factors (BCF) and two MEDV descriptors of 58 PCBs. The MI model showed a good estimation ability with a correlation coefficient ( r) of 0.9605 and a high stability with a leave-one-out cross-validation correlation coefficient ( q) of 0.9564. The MEDV-base model (MI) is easier to use than the splinoid poset method reported by Ivanciuc et al. [Ivanciuc, T., Ivanciuc, O., Klein, D.J., 2006. Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol. Divers. 10, 133–145] and gives a better statistics than molecular connectivity index (MCI)-base model developed by Hu et al. [Hu, H.Y., Xu, F.L., Li, B.G., Cao, J., Dawson, R., Tao, S., 2005. Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models. Water Environ. Res. 77, 87–97]. Main structural factors influencing the BCF of PCBs are the substructures expressed by two atomic groups C and CH . 58 PCBs were divided into an “odd set” and “even set” in order to ensure the predicted potential of the MI for the external samples. It was shown that three models, MI, MO for “odd set”, and ME for “even set”, can be used to predict the BCF of remaining 152 PCBs in which the experimental BCFs are not available.

Predicting the Gas Chromatographic Relative Retention Time of Polybrominated Diphenyl Ethers by MEDV-13 Descriptors

Polybrominated diphenyl ethers (PBDEs) have been used extensively over the past two decades as flame or fire retardants added into many products such as textiles, electronic equipment and insulation material. PBDEs are now ubiquitous environmental pollutants and have been detected in biotic and abiotic matrices that include fish, birds, sediments, air, marine mammals, and human plasma and milk. The purpose of the paper is to develop some models which can predict the gas chromatographic relative retention time of PBDEs on seven stationary phases. The molecular electronegativity distance vector based on 13 atomic types (MEDV-13) were employed to describe the structural changes of PBDEs. Using the variables selection and modeling based on the prediction (VSMP), some four-variable linear equations from 80 PBDEs in the training sets with the correlation coefficient (r2) >0.98 and the root mean square errors of estimation <0.029 (except for CP-Sil19) were established. The standard regression coefficient indicated that MEDV descriptor x91 (interaction between the substructure –Br and –Br) was the most important descriptor to affect the RRT values of PBDEs. At the same time, it was found that the number of the ortho-substituent bromines and the number of bromine atoms in PBDE molecule may improve the predictive quality.

QSPR study of polychlorinated diphenyl ethers by molecular electronegativity distance vector (MEDV-4)

Polychlorinated diphenyl ethers (PCDEs) have received more and more concerns as a group of ubiquitous potential persistent organic pollutants (POPs). By using molecular electronegativity distance vector (MEDV-4), multiple linear regression (MLR) models are developed for sub-cooled liquid vapor pressures ( P L), n-octanol/water partition coefficients ( K OW) and sub-cooled liquid water solubilities ( S W,L) of 209 PCDEs and diphenyl ether. The correlation coefficients ( R) and the leave-one-out cross-validation (LOO) correlation coefficients ( R CV) of all the 6-descriptor models for log P L, log K OW and log S W,L are more than 0.98. By using stepwise multiple regression (SMR), the descriptors are selected and the resulting models are 5-descriptor model for log P L, 4-descriptor model for log K OW, and 6-descriptor model for log S W,L, respectively. All these models exhibit excellent estimate capabilities for internal sample set and good predictive capabilities for external samples set. The consistency between observed and estimated/predicted values for log P L is the best ( R = 0.996, R CV = 0.996), followed by log K OW ( R = 0.992, R CV = 0.992) and log S W,L ( R = 0.983, R CV = 0.980). By using MEDV-4 descriptors, the QSPR models can be used for prediction and the model predictions can hence extend the current database of experimental values.

Estimation and Prediction of Relative Retention Indices of Polychlorinated Naphthalenes in GC with Molecular Electronegativity Distance Vector

A molecular electronegativity-distance vector (MEDV) is developed to encode the information about molecular structures of polychlorinated naphthalenes (PCNs) and relate to their relative retention indices (RRIs). A six-parameter equation of 62 PCNs is developed using the multiple linear regression (MLR) and cross validation (CV) of leave-one-out (LOO) technique and the statistical results indicate that the correlation coefficient, standard deviation and the F statistic are 0.9879, 0.9698; 39.03, 40.06 and 371.97, 352.47, respectively. The descriptor variables in the MED vector are analyzed using the stepwise multiple regression (SMR) technique, and only one variable, m 13 descriptor, is selected to establish another reduced QSRR model. Results of both the estimation for the RRIs of 62 PCNs and the prediction for that of other 13 PCNs plus the naphthalene show that the MEDV descriptors not only serve as a good molecular structural descriptor for PCNs, but also have powerful prediction abilities in estimating and predicting their chromatographic retention.

Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector

Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.

Quantitative structure-activity relationship of estrogen activities of bisphenol A analogs

The molecular electronegativity-distance vector (MEDV) is employed to describe the chemical structure of bisphenol A analogs and their correlated estrogen activities. The result shows that the constructed models have good predictability and indicates substructures that may influence estrogen activities of chemicals.

Molecular electronegativity-distance vector (MEDV-4): a two-dimensional QSAR method for the estimation and prediction of biological activities of estradiol derivatives

By using molecular electronegativity-distance vector (MEDV-4) approach, a two-dimensional quantitative structure–activity relationship (2D-QSAR) model has been developed to estimate and predict the binding activities of a series of 11β-, 16α-, 17α-substituted estradiols for estrogen receptor. Considering the contribution of each non-hydrogen atom, the MEDV-4 exhibits high relevance to the structural properties. The 10-parameter model yields a R 2=0.870 and a leave-one-out procedure cross-validation R CV 2=0.616. The parameters were screened by the stepwise multiple regression approach and the newly formed optimal models had R 2=0.852 and R CV 2=0.747 when the number of parameters are taken as m=6. The performance of the models is better than that reported in literature, and had high correlation with the typical parameters (hydrophobic parameter π 11, molecular refraction MR 11 and indicator variable I) used in literature. The MEDV-4 model can provide a simple and rapid way to investigate the binding activities of estradiols.