Toxicity of Selected Imidazolium-based Ionic Liquids on Caenorhabditis elegans： a Quantitative Structure-Activity Relationship Study

Due to the large number of ionic liquids （ILs） and their potential environmental risk, assessing the toxicity of ILs by ecotoxicological experiment only is insufficient. Quantitative structure- activity relationship （QSAR） has been proven to be a quick and effective method to estimate the viscosity, melting points, and even toxicity of ILs. In this work, the LC50 values of 30 imidazolium-based ILs were determined with Caenorhabditis elegans as a model animal. Four suitable molecular descriptors were selected on the basis of genetic function approximation algorithm to construct a QSAR model with an R^2 value of 0.938. The predicted lgLC50 in this work are in agreement with the experimental values, indicating that the model has good stability and predictive ability. Our study provides a valuable model to predict the potential toxicity of ILs with different sub-structures to the environment and human health.

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.

New Descriptors of Amino Acids and Its Applications to Peptide Quantitative Structure-activity Relationship

A new set of descriptors, HSEHPCSV （component score vector of hydrophobic, steric, and electronic properties together with hydrogen bonding contributions）, were derived from principal component analyses of 95 physicochemical variables of 20 natural amino acids separately according to different kinds of properties described, namely, hydrophobic, steric, and electronic properties as well as hydrogen bonding contributions. HSEHPCSV scales were then employed to express structures of angiotensin-converting enzyme inhibitors, bitter tasting thresholds and bactericidal 18 peptide, and to construct QSAR models based on partial least square （PLS）. The results obtained are as follows： the multiple correlation coefficient （R2cum） of 0.846, 0.917 and 0.993, leave-one-out cross validated Q2cm of 0.835, 0.865 and 0.899, and root-mean-square error for estimated error （RMSEE） of 0.396, 0.187and 0.22, respectively. Satisfactory results showed that, as new amino acid scales, data of HSEHPCSV may be a useful structural expression methodology＇for the studies on peptide QSAR （quantitative structure-activity relationship） due to many advantages such as plentiful structural information, definite physical and chemical meaning and easy interpretation.

Quantitative Structure-activity Relationship Study on the Antioxidant Activity of Carotenoids

Carotenoids are a family of effective active oxygen scavengers, which can reduce the danger of occurrence of chronic diseases such as cardiovascular disease, cataract, cancer, and so on. The quantitative structure-activity relationship （QSAR） equation between carotenoids and antioxidant activity was established by quantum chemistry AM1, molecular mechanism （MM＋） and stepwise regression analysis methods, and the model was evaluated by leave-one-out approach. The results showed that the significant molecular descriptors related to the antioxidant activity of carotenoids were the energy difference （E_HL） between the lowest unoccupied molecular orbital （LUMO） and the highest occupied molecular orbital （HOMO） and ionization energy （Eiso）. The model showed a good predictive ability （Q^2 〉 0.5）.

Quantitative Structure-activity Relationship(QSAR) Study of Toxicity of Substituted Aromatic Compounds to Photobacterium Phosphoreum

With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.

Quantitative Structure-activity Relationship Studies on the Antioxidant Activity and Gap Junctional Communication of Carotenoids

The antioxidant and gap junctional communication（GJC） activities of carotenoids are known to be the two main anticancer mechanisms.Quantitative structure-activity relationship（QSAR） models of the two activities were developed using stepwise regression and multilayer perceptron neural network based on the calculated descriptors of quantum chemistry.The results showed that the significant molecular descriptor related to the antioxidant activity of carotenoids was the HOMO-LUMO energy gap（EHL） and the molecular descriptor related to the GJC was the lowest unoccupied molecular orbital energy（ELUMO）.The two models of antioxidant activity both showed good predictive power,but the predictive power of the neural network QSAR model of antioxidant activity was better.In addition,the two GJC models have similar,moderate predictive power.The possible mechanisms of antioxidant activity and GJC of carotenoids were discussed.

Quantitative Structure-activity Relationship Models of Monomer Reactivity

The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative structure-activity relationship(QSAR)model for the reactivity parameter lnQ was developed based on five descriptors(NAF,NOF,EαLUMO,EβHOMO,and EβLUMO)and 69 monomers with the root mean square(rms)error of 0.61.The optimal MLR model of the parameter e obtained from five descriptors(TOcl,NpN,NSO,EαHOMO and DH)and 68 monomers produced rms error of 0.42.Compared with previous models,the two optimal MLR models in this paper show satisfactory statistical characteristics.The feasibility of combining 2D descriptors obtained from the monomers and 3D descriptors calculated from the radical structures(formed from monomers+H )to predict parameters Q and e has been demonstrated.

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.

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.

Some substrates of P-glycoprotein targeting <i>β</i>-amyloid clearance by quantitative structure-activity relationship (QSAR)/membrane-interaction (MI)-QSAR analysis

The pathogenesis of Alzheimer’s disease (AD) putatively involves a compromised blood-brain barrier (BBB). In particular, the importance of brain-to-blood transport of brain-derived metabolites across the BBB has gained increasing attention as a potential mechanism in the pathogenesis of neurodegenerative disorders such as AD, which is characterized by the aberrant polymerization and accumulation of specific misfolded proteins, particularly β-amyloid (Aβ), a neuropathological hallmark of AD. P-glycoprotein (P-gp), a major component of the BBB, plays a role in the etiology of AD through Aβ clearance from the brain. Our QSAR models on a series of purine-type and propafenone-type substrates of P-gp showed that the interaction between P-gp and its modulators depended on Molar Refractivity, LogP, and Shape Attribute of drugs it transports. Meanwhile, another model on BBB partitioning of some compounds revealed that BBB partitioning relied upon the polar surface area, LogP, Balaban Index, the strength of a molecule combined with the membrane-water complex, and the changeability of the structure of a solute-membrane-water complex. The predictive model on BBB partitioning contributes to the discovery of some molecules through BBB as potential AD therapeutic drugs. Moreover, the interaction model of P-gp and modulators for treatment of multidrug resistance (MDR) indicates the discovery of some molecules to increase Aβ clearance from the brain and reduce Aβ brain accumulation by regulating BBB P-gp in the early stages of AD. The mechanism provides a new insight into the therapeutic strategy for AD.

Quantitative Structure-Activity Relationship Study of Some Antipsychotics by Multiple Linear Regressions

The retention behavior and lipophilicity parameters of some antiphychotics were determined using reversed-phase thin layer chromatography. Quantitative structure-activity relationships studies have been performed to correlate the molecular characteristics of observed compounds with their retention as well as with their chromatographically determinated lipophilicity parameters. The effect of different organic modifiers (acetone, tetrahydrofuran, and methanol) has been studied. The retention of investigated compounds decreases linearly with increasing concentration of organic modifier. The chemical structures of the antipsychotics have been characterized by molecular descriptors which are calculated from the structure and related to chromatographically determinated lipophilicity parameters by multiple linear regression analysis. This approach gives us the possibility to gain insight into factors responsible for the retention as well as lipophilicity of the investigated set of the compounds. The most prominent factors affecting lipophilicity of the investigated substances are Solubility, Energy of the highest occupied molecular orbital, and Energy of the lowest unoccupied molecular orbital. The obtained models were used for interpretation of the lipophilicity of the investigated compounds. The prediction results are in good agreement with the experimental value. This study provides good information about pharmacologically important physico-chemical parameters of observed antipsychotics relevant to variations in molecular lipophilicity and chromatographic behavior. Established QSAR models could be helpful in design of novel multitarget antipsychotic 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.

Prediction of Stability for Polychlorinated Biphenyls in Transformer Insulation Oil Through Three-dimensional Quantitative Structure-activity Relationship Pharmacophore Model and Full Factor Experimental Design

Based on the obtained data of half-lives（t1/2） for 31 polychlorinated biphenyl congeners（PCBs）, 3D quantitative structure-activity relationship（QSAR） pharmacophore was used to establish a 3D QSAR model to predict the t1/2 values of the remaining 178 PCBs, using the structural parameters as independent variables and lgt1/2 values as the dependent variable. Among this process, the whole data set（31 compounds） was divided into a training set（24 compounds） for model generation and a test set（7 compounds） for model validation. Then, the full factor experimental design was used to research the potential second-order interactional effect between different substituent positions, obtaining the final regulation scheme for PCB. At last, a 3D QSAR pharmacophore model was established to validate the reasonable regulation targeting typical PCB with respect to half-lives and thermostability. As a result, the cross-validation correlation coefficient（q2） obtained by the 3D QSAR model was 0.845（〉0.5） and the coefficient of determination（r2） obtained was 0.936（〉0.9）, indicating that the models were robust and predictive. CoMSIA analyses upon steric, electrostatic and hydrophobic fields were 0.7%, 85.9%, and 13.4%, respectively. The electrostatic field was determined to be a primary factor governing the tt/2. From CoMSIA contour maps, tl/2 increased when substi- tuents possessed electropositive groups at the 2＇-, 3-, Y-, 5- and 5＇- positions and electronegative groups at the 3-, 3＇-, 5-, 6- and 6＇- positions, which could increase the PCB stability in transformer insulation oil. Modification of two typical PCB congeners（PCB-77 and PCB-81） showed that the lgtl/2 for three selected modified compounds increased by 13%（average ratio） compared with that of each congener and the thermostability of them were higher, validating the reasonability of the regulatory scheme obtained from the 3D QSAR model. These results are expected to be beneficial in predicting tl/2 values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the stability of PCBs.

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.

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.

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.

Quantitative determination of PFC3D microscopic parameters

It is important to calibrate micro-parameters for applying partied flow code(PFC)to study mechanical characteristics and failure mechanism of rock materials.Uniform design method is firstly adopted to determine the microscopic parameters of parallel-bonded particle model for three-dimensional discrete element particle flow code(PFC3D).Variation ranges of microscopic of the microscopic parameters are created by analyzing the effects of microscopic parameters on macroscopic parameters(elastic modulus E,Poisson ratio v,uniaxial compressive strengthσc,and ratio of crack initial stress to uniaxial compressive strengthσci/σc)in order to obtain the actual uniform design talbe.The calculation equations of the microscopic and macroscopic parameters of rock materials can be established by the actual uniform design table and the regression analysis and thus the PFC3D microscopic parameters can be quantitatively determined.The PFC3D simulated results of the intact and pre-cracked rock specimens under uniaxial and triaxial compressions(including the macroscopic mechanical parameters,stress−strain curves and failure process)are in good agreement with experimental results,which can prove the validity of the calculation equations of microscopic and macroscopic parameters.

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.

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.

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.