Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs

Quantitative structure-biodegradability relationships （QSBRs） were established to develop predictive models and mechanistic explanations for acid dyestuffs as well as biological activities. With a total of four descriptors, molecular weight （MW）, energies of the highest occupied molecular orbital （EHOMO）, the lowest unoccupied molecular orbital （ELUMO）, and the excited state （EES）, calculated using quantum chemical semi-empirical methodology, a series of models were analyzed between the dye biodegradability and each descriptor. Results showed that EHOMO and Mw were the dominant parameters controlling the biodegradability of acid dyes. A statistically robust QSBR model was developed for all studied dyes, with the combined application of EHOMO and Mw. The calculated biodegradations fitted well with the experimental data monitored in a facultative-aerobic process, indicative of the reliable prediction and mechanistic character of the developed model.

Quantum Chemical Study on Structure-activity Relationship of Several Kinds of Drugs

The structure-activity relationship of several drugs with similar structure has been investigated by using ab initio method. The relation between the dipole moments and biological activities of these drugs was judged after comparing their geometric structures, dipole moments and inhibitory concentrations. In principle, new drug molecule could be reasonably designed by altering the place of groups and ultimately, the potential drug could be screened by comparing the dipole moments of obtained molecules.

Predicting quantitative structure-activity relationship of substituted 17α-acetoxyprogesterones by molecular hybridization electronegativity-distance vector

A set of novel structural descriptors (molecular hybridization electronegativity-distance vector, VMEDh) was put forward, and the quantitative structure–activity relationship (QSAR) of a series of 17α-Acetoxyprogesterones (APs) was investigated. Taking into account the effect of various hybridized orbits on atomic electronegativities, we developed the structure descriptors with amended electronegativities to build a QSAR model. The 10-parameter model based on VMEDh yields a correlation coefficient R=0.972 and standard deviation SD=0.262, which are more desirable than those of the previous molecular electonegativity-distance vector (MEDV-4) (R=0.969, SD=0.275). By stepwise multiple linear regression, several parameters are selected to construct optimal models. The 7-parameter model based on VMEDh has R=0.960 and SD=0.276; its correlation coefficient (RCV) and standard deviation (SDCV) for leave-one-out procedure crossvalidation are respectively RCV=0.890 and SDCV=0.445. The 6-parameter MEDV-4 model has R=0.946, SD=0.304, RCV=0.903 and SDCV=0.406. It is demonstrated that VMEDh has desirable estimation performance and good predictive capability for this series of chemical compounds.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.

QSAR Studies on the Inhibitory Activityof Levofloxacin-thiadiazole HDACi Conjugates to Histone Deacetylases

A molecular electronegativity distance vector(M)based on 13 atomic types has been used to describe the structures of 19 conjugates(LHCc)of levofloxacin-thiadiazole HDAC inhibitor(HDACi)and related inhibitory activities(pH,i=1,2,6)of LHCc against histone deacetylases(HDACs,such as HDAC1,HDAC2 and HDAC6).The quantitative structure-activity relationships(QSAR)were established by using leaps-and-bounds regression analysis for the inhibitory activities(pH)of 19 above compounds to HDAC1,HDAC2 and HDAC6 along with M.The correlation coefficients(R~2)and the leave-one-out(LOO)cross validation Rfor the pH,pHand pHmodels were 0.976 and 0.949;0.985 and 0.977;0.976 and 0.932,respectively.The QSAR models had favorable correlations,as well as robustness and good prediction capability by R~2,F,R~2,A,Fand Vtests.Validated by using 3876 training sets,the models have good external prediction ability.The results indicate that the molecular structural units:–CH–(g=1,2),–NH,–OH,=O,–O–and–S–are the main factors which can affect the inhibitory activity of pH,pHas well as pHbioactivities of these compounds directly.Accordingly,the main interactions between HDACs inhibitor and HDACs are hydrophobic interaction,hydrogen bond,and coordination with Znto form compounds,which is consistent with the results in reports.

Determination and QSAR Study on the Toxicity of Substituted Phenol against Qinghaiensis sp (Q67)

Toxicities （-1gEC50） of 16 phenolic compounds against Q67 were determined, and structural parameters as well as thermodynamic parameters of these compounds were obtained through fully optimized calculations by using B3LYP method of density functional theory （DFT） at the 6-311G^＊＊ level. Moreover, a 3-parameter （molecular average polarizability （α）, heat energy corrected value （Eth） and the most positive hydrogen atomic charge （qH^＋）） correlation model with R^2 = 0.981 and q^2 = 0.967 to predict -1gEC50 was obtained from experimental data based on the above-mentioned parameters as theoretical descriptors. Therein a was the most significant on -1gEC50. Variance Inflation Factors （VIF）, t-value and cross-validation were applied to verify the model, confirming that the resultant model has fairly better stability and predictive ability to predict -1gEC50 of similar compounds.

On QSAR Study of Stereoselectivity for Wittig Reaction

Molecular structures of reactants were characterized by molecular electronegativity distance vector （VHMED） considering hydrogen association. A reasonable molecular modeling equation with 4-parameters was achieved for quantitative structure-property/activity relationship （QSPR/QSAR） by stepwise multiple regression （SMR） that the variable was introduced item by item in significant level order. A high correlation coefficient （R = 0.980） demonstrates that the model is able to well express a quantitative relation between stereoselectivity and the reactant structures as quantitative structure-reactivity/stereoselectivity relationship （QSRR/QSSR）. The multiple correlation coefficient （Rcv= 0.964） was tested through cross-validation with the leave-one-out （LOO） procedure. The above results show that the model possesses high estimation stability and good prediction ability between the amount of both cis and trans isomers in products and reactants.

Toxicity Measurement of Halogeno-benzene against Vibrio Qinghaiensis (Q67) and Their 2D,3D-QSAR Study

Toxicities （–lgEC50） of 16 halogeno-benzenes against vibrio qinghaiensis （Q67） were measured systematically, and their 2D-QSAR model （R2=0.875, q2=0.821） was established, which included two parameters： averaged polarizability （α） and total energy （TE）. The proposed model indicated that the toxicities of this kind of compounds were proportionate to α, i.e., their toxicities were relative to the molecular volume. Furthermore, 3D-QSAR model （R2=0.929, q2=0.712） of –lgEC50 was proposed by using comparative molecular force field （CoMFA） based on the molecular simulation. To our interest, 3D-QSAR model suggested that the hydrophobicity of substituents was the dominating factor for the toxicities, the electrostatic effect was the secondly important, and the steric field gave the least contribution. Comparably, the prediction ability of the 3D-QSAR model is slightly more advantageous than that of 2D-QSAR, and they can be used complementally in the toxicity description of this kind of compounds.

QSAR of Estrogen of Bisphenol A with 3D Vector of Atomic Property Correlation

Considering atomic property vector and atomic correlative function, the 3-dimensional structural vector of atomic property correlation （3D-VAPC）, a novel descriptor,is defined to characterize a 3-dimensional molecular structure by introducing self-adaptability regulation mechanism and the idea of orientating to customers. Characterizing the structures of 25 bisphenol A compounds by this vector, the QSAR models of three kinds of estrogen activities （ER affinities, gene induction and cell proliferation） have high multiple correlation coefficient （Rcum^2=0.933, 0.813, 0.959） and cross verification coefficient （Qcum^2=0.847, 0.953, 0.798） by support vector machine （SVM）, which suits for nonlinear circumstances. The above results show that the models successfully express the correlation between structure and three kinds of estrogen activities. Therefore, 3D-VAPC exactly reflects the molecular structural information and SVM method correctly describes the correlation between information and property of the compounds.

Genotoxicity of substituted nitrobenzenes and the quantitative structure－activity relationship

The genotoxicity of 22 substituted nitrobenzenes were evaluated by the chromosome aberrations test in in vitro human peripheral lymphocytes.18 of 22 compounds exhibit genotoxic activities.Quantitative structure-activity relationship model was established to correlate the genotoxicity of substituted nitrobenzenes with the characteristics of the substituents on benzene ring.

Development of QSAR Model for Predicting the Mutagenicity of Aromatic Compounds

Quantitative structure-activity relationship （QSAR） model was developed for pre- dicting the mutagenicity of aromatic compounds. The log revertants data of S. typhimurium TA98 strain from Ames test have been collected. 225 aromatic compounds were randomly divided into the training set with 186 molecules and test set with 39 molecules. Multiple linear regression （MLR） analysis was used to select six descriptors from thousands of descriptors calculated by semi- empirical AM l and E-dragon methods. The final QSAR model with six descriptors was internal and external validated. In addition, to validate the utility of our QSAR model for the chemical evaluation, three aromatic compounds were taken to test the predictive ability and reliability of the model experimentally. The compounds selected for testing were not based on the predictions, thus spanning the range of predicted probabilities. The subsequently generated results of the Ames test were in good correspondence with the predictions and confirmed this approach as a useful means of predicting likely mutagenic risk of aromatic compounds.

QSAR Study on Some N-[5-(2-Furanyl)-2-methyl-4-oxo-4H-thieno[2,3-d]pyrimidin-3-yl]-carboxamide and 3-Substituted-5-(2-furanyl)-2-methyl-3H-thieno-[2,3-d]pyrimidin-4-ones Using Three-dimensional Holographic Vector of Atomic Interaction Field

Study on the quantitative structure-activity relationship （QSAR） of 26 compounds, N-[5-（2-furanyl）-2-methyl-4-oxo-4H-thieno[2,3-d]pyrimidin-3-yl]-carboxamide and 3-substituted- 5-（2-furanyl）-2-methyl-3H-thieno[2,3-d]pyrimidin-4-ones, with three-dimensional holographic vector of atomic interaction field （3D-HoVAIF） was carried out. SMR-PLS QSAR models have been created and good correlation coefficients and cross-validated correlation coefficients were obtained. The result shows that the models have good prediction capability and favorable stability and the 3D-HoVAIF is applicable to the molecular structural characterization and biological activity prediction.

QSAR for Photodegradation Activity of Polycyclic Aromatic Hydrocarbons in Aqueous Systems

The relationship between chemical structures and photodegradation activity of 12 PAHs is studied using DFT and HF methods, and stepwise multiple linear regression analysis method. The equilibrium geometries and vibration frequency have been investigated by considering Solvent effects using a selfconsistent reaction field based on the polarizable continuum model. With DFT and HF methods, different quantum chemical structural descriptors are obtained by quantum chemical calculation and the results with DFT method are better for QSAR model. It is concluded that the photodegradation activity is closely related to its molecular structure. In the regression analysis, the main factors affecting photodegradation rate include the energy of the highest occupied orbital EHOMO and the number of six-carbon benzene ring N1, and the QSAR model successfully established is logkb = 6.046 + 54.830EHOMO + 0.272N1. Statistical evaluation of the developed QSAR shows that the relationships are statistically significant and the model has good predictive ability. EHOMO is the most important factor influcing the photodegradation of PAHs, because the higher EHOMO is, the more easily electron will be excited and the more easily molecular will be degraded. Comparison of the photodegradation of PAHs with their biodegradation shows that the committed step of biodegradation is that the effects of microorganisms make the chemical bond break, while in the committed step of photodegradation PAHs eject electrons.

QSAR Studies on Influenza Neuraminidase Inhibitors——Acylthiourea Analogue

The quantitative structure-activity relationship （QSAR） of 30 acylthiourea analogues was studied by using a three-dimensional holographic vector of atomic interaction field （3D-HoVAIF） to describe their chemical structures. The descriptors obtained were screened by stepwise multiple regression （SMR） and a partial least-squares （PLS） regression model was built. The correlation coefficient r^2 of the established model and Leave-One-Out （LOO） Cross-Validation （CV） correlation coefficient q^2 are 0.624 and 0.409, respectively. The model has favorable stability and good prediction capability, and further QSAR analysis showed that hydrophobic interaction has the most important effect on the activity of acylthiourea analogue and 3D-HoVAIF was applicable to the molecular structural characterization and biologicalactivity prediction.

Quantitative Structure Activity Relationship Studies of Benzoxazinone Derivative Antithrombotic Drug Using New Three-dimensional Structure Descriptors

A novel three-dimensional holographic vector of atomic interaction field（3D-HoVAIF） was used to describe the chemical structures of 23 benzoxazinone derivatives as antithrombotic drugs.Here a quantitative structure activity relationship（QSAR） model was built by partial least-squares（PLS） regression.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The correlation coefficients of established PLS model,leave-one-out（LOO） cross-validation,and predicted values versus experimental ones of external samples were R2=0.899,RCV2=0.854 and Qext2=0.868,respectively.These values indicated that the built PLS model had both favorable estimation stability and good prediction capabilities.Furthermore,the satisfactory results showed that 3D-HoVAIF could preferably express the information related to the biological activity of benzoxazinone derivatives.

Quantitative structure-activity relationship of compounds binding to estrogen receptor β based on heuristic method

Estrogen compounds may pose a serious threat to the health of humans and wildlife. The estrogen receptor （ER） exists as two subtypes, ERα and ERβ. Compounds might have different relative affinities and binding modes for ERα and ERβ. In this study, the heuristic method was performed on 31 compounds binding to ERβ to select 5 variances most related to the activity （LogRBA） from 1524 variances, which were then employed to develop the best model with the significant correlation and the best predictive power （γ^2 = 0.829, q^2LOO = 0.742, γ^2pred = 0.772, q^2ext = 0.724, RMSEE = 0.395） using multiple linear regression （MLR）. The model derived identified critical structural features related to the activity of binding to ERβ. The applicability domain （AD） of the model was assessed by Williams plot.

Quantitative Correlation of Chromatographic Retention and Acute Toxicity for Alkyl(1-phenylsulfonyl) Cycloalkane Carboxylates and Their Structural Parameters by DFT

Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.

Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods

In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.

Quantitative Correlation of the Acute Toxicity of Phenylthio-carboxylates with Their Structural and Thermodynamic Parameters by DFT Calculation

Phenylthio-carboxylates were computed at the B3LYP/6-31G* level with DFT method. Based on linear solvation energy theory, the structural parameters were firstly taken as theoretical descriptors, and the corresponding linear solvation energy relationship (LSER) equation (r = 0.8989) to the toxicity of photobacterium phosphoreum (–lgEC50) was thus obtained. Then the structural and thermodynamic parameters were taken as theoretical descriptors, and as a result the other corresponding correlation equation (r = 0.9274) relating to –lgEC50 was provided. The two equations achieved in this work by B3LYP/6-31G* are both more advantageous than that from AM1.

Correlation of Quantitative Structure and Inhibition Phytotoxicity for Aromatic Compounds Using Ab Initio Method

29 aromatic compounds were computed at the HF/6-31G^＊ level. Based on linear solvation energy theory firstly, the parameters of molecular structure were taken as theoretical descriptors, and the corresponding linear solvation energy relationship （LSER） （r^2= 0.8993, q^2=0.8559） between the structural parameters and inhibition phytotoxicity to the seed germination rate of cucumis （-lgGC50） was thus obtained. Then the parameters of molecular structure and thermodynamics were taken as theoretical descriptors, and as a result the other corresponding correlation equation （r^2=0.9268, q^2=0.8960） relating to -lgGC50 was achieved. The two equations obtained in this work by HF/6-31G^＊ are both more advantageous than that from AM 1.