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 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.

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.

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(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.

On quantitative structure-activity relationships between hydrazine derivatives and β irradiation

In this study, solutions of hydrazine and its derivatives were irradiated using a pulsed electron beam to determine the half-reaction time of radiolysis. 3 D structures of the hydrazine derivatives were optimized, and their energies were calculated using density functional theory with the B3 LYP method and 6-311 +(3 d, 3 p) basis set.For the first time, the 3 D quantitative structure-activity relationship(QSAR) equation describing the relationship between the hydrazine derivative structures and rate of radiolysis has been established using SPSS software.Pearson correlation analysis revealed a close correlation between the total energies of the molecules and half-reaction times. In the QSAR equation, Y =-7583.464 +54.687 X_1+94333.586 X_2,Y,X_1,and X_2 are the half-reaction time, total energy of the molecule, and orbital transition energy, respectively. The significance levels of the regression coefficients were 0.006 and 0.031, i.e., both less than 0.05. Thus, this model fully explains the relationship between hydrazine derivatives and β radiolysis stability.The results show that the total energy of the molecule and orbital transition energy are the main factors that influence the β radiolysis stability of these hydrazine derivatives.

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.

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.

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.

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 study on the reductive dehalogenation potency of the halogenated aromatics

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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.

Synthesis and Structure-Antitumor Activity of 4,6-Diamino-1,2-Dihydro-2 ,2-Dimethyl-1-(Substituted-Naphthyl- 2)-1,3,5-Triazines

Introduction 4,6-Diamino-1,2-dihydro-2,2-dimethyl-1-substituted-1,3,5-triazines (Ⅰ) are dihydrofolate reductase(DHFR) inhibitors, hence they possess the inhibition to the growth of bacteria and cancer cells. Baker’s antifolate (Ⅱ) has shown promise as an antitumor agertt in clinical trials. The study of the structure-activity relationships of I shows that the inhibition to vertebrate DHFR is significantly influenced by the hydrophobicity of 1-substituent, i.e., the stronger the hydrophobicity of the 1-substituent, the more potent the inhibition of the compound to vertebrate DHFR.

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.

Quantitative structure-activity relationships of antimicrobial fatty acids and derivatives against Staphylococcus aureus

Fatty acids and derivatives(FADs)are resources for natural antimicrobials.In order to screen for additional potent antimicrobial agents,the antimicrobial activities of FADs against Staphylococcus aureus were examined using a microplate assay.Monoglycerides of fatty acids were the most potent class of fatty acids,among which monotridecanoin possessed the most potent antimicrobial activity.The conventional quantitative structure-activity relationship(QSAR)and comparative molecular field analysis(CoMFA)were performed to establish two statistically reliable models(conventional QSAR:R2=0.942,Q 2 LOO=0.910;CoMFA:R 2=0.979,Q 2=0.588,respectively).Improved forecasting can be achieved by the combination of these two models that provide a good insight into the structureactivity relationships of the FADs and that may be useful to design new FADs as antimicrobial agents.

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.

Artificial Neural Networks Applied to the Quantitative Structure-Activity Relationship Study of Para-substituted Phenols

The artificial neural network (ANN) model with back-propagation of error is used to study the quantitative structure-activity relationship of para-substituted phenol derivatives between the biological activity and the physicochemical property parameters. Network parameters are optimized, and an empirical rule for dynamically adjusting the network’s learning rate is proposed to improve the network’s performance. The results showthat the three-layer ANN model gives satisfactory performance, with f(x)=1/(1+exp(-x)) as the network node’s input-output transformation function and the number of hidden nodes 10. The network gives the mean square error (rose) of 0.036 when predicting the biological activity of 26 para-substituted phenol derivatives. This result compares favourably with that obtained by the conventional methods.

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.

Holographic quantitative structure-activity relationship for prediction of the toxicity of polybrominated diphenyl ether congeners

Polybrominated diphenyl ether congeners (PBDEs) might activate the AhR (aromatic hydrocarbon receptor) signal transduction, and thus might have an adverse effect on the health of humans and wildlife. Because of the limited experimental data, it is important and necessary to develop structure-based models for prediction of the toxicity of the compounds. In this study, a new molecular structure representation, molecular hologram, was employed to investigate the quantitative relationship between toxicity and molecular structures for 18 PBDEs. The model with the significant correlation and robustness (r 2 = 0.991, q 2 LOO = 0.917) was developed. To verify the robustness and prediction capacity of the derived model, 14 PBDEs were randomly selected from the database as the training set, while the rest were used as the test set. The results generated under the same modeling conditions as the optimal model are as follows: r 2 = 0.988, q 2 LOO = 0.598, r 2 pred = 0.955, and RMSE (root-mean-square of errors) = 0.155, suggesting the excellent ability of the derived model to predict the toxicity of PBDEs. Furthermore, the structural features and molecular mechanism related to the toxicity of PBDEs were explored using HQSAR color coding.

Three-Dimensional Quantitative Structure-Activity Relationships of flavonoids and estrogen receptors based on docking

Flavonoids are endocrine disrupting compounds that occur ubiquitously in foods of plant origin.The Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) model based on ligand-receptor docking is established between 20 flavonoids and estrogen receptor alpha (ERα),which may provide further theoretical basis for research on the relationship between flavones and estrogen.Comparative molecular field analysis (CoMFA) was employed and the best results of cross-validation and non cross validation were 0.845 and 0.988,respectively.Correspondingly,molecular similarity index analysis (CoMSIA) was employed and the results of cross-validation and non cross validation were 0.670 and 0.990,respectively.The CoMFA/CoMSIA and docking results reveal the structural features for estrogen activity and key amino acid residues in binding pocket,and provide an insight into the interaction between the ligands and these amino acid residues.