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 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 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.

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.

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.

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 study on the reductive dehalogenation potency of the halogenated aromatics

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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.

A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Studies on the Relationship between the Helical Structure and Optical Activity of Some Chiral Cyclic Esters Ⅱ

A method for the analysis of the relationship between the helical structure and optical activity was proposed by the study of the conformations and X-ray diffraction structures of some cyclic esters prepared by esterification of L-(-)-2.3-O-methylidene threitol and L-(+)-2,3-O-isopropplidene threitol with alkanedioyl dichlorides and o-,m-,and p-phthaloyl dichlorides.

Quantitative Structure-Property Relationship on Prediction of Surface Tension of Nonionic Surfactants

A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, the Kier & Hall index of zero order (KH0) of the hydrophobic segment of surfactant and a quantum chemical one, the heat of formation (fHD) of surfactant molecules. The established general QSPR between the surface tension and the descriptors produces a correlation coefficient of multiple determination, 2r=0.9877, for 30 studied nonionic surfactants.

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.

Quantitative Models for the Structure and Photodegradation of Polycyclic Aromatic Hydrocarbons

Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.

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.

Computation of Structure Activity and Design of Chalcone Derivatives

Based on the antitumor activity of chalcone derivatives and their structure, the structure-activity relationship of chalcone derivatives was analyzed by CoMFA (comparative molecular field analysis) method and then the 3D-QSAR (the three dimensional quantitative structures activity relationship) model was established. The analytical results showed that the model had good stability and prediction ability with cross-validated Q2 and non-cross-validated R2 values of 0.527 and 0.995, respectively. The contour map of the model explained the relationship between the structure of chalcone derivatives and antitumor activity, and could be analyzed to design antitumor chalcone derivatives. We designed some structure of chalcone derivatives and calculated their antitumor activity. In this paper, 24 chalcone derivatives were studied by CoMFA, 3D-QSAR, molecular design, which provided theoretical for designing good activities chalcone derivatives.

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.

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.