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 dyestu?s 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 (EHL) 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 (Q2 > 0.5).

Study on 3D Quantitative Structure-Activity Relationships of Taxoid

A series of 94 taxol analogues exhibiting antitumor activity were investigated using comparative molecular ficld analsis (CoMFA) method. The most optimal CoMFA from 80 compounds selected randomly . yielded a two-components model, with siginficant cross-validation r cv of 0.640 and conventional r of 0.868. The robustness of the CoMFA model was further validatal by prediedon Of 14 test set componds. The CoMFA model explained present SAR and prtcularly the importance of the hydroxyl group at C-2’ position and the stereochemistry at C-2’ and C-3’

Genotoxicity of substituted nitrobenzenes and the quantitative structure－activity relationship

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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 Q2cum 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.

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.

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.

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.

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 of TIBO HIV-1 Inhibitors

Density functional theory (DFT) was used to calculate a set of molecular descriptors (properties) for 14 TIBO derivatives with anti-HIV activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed in order to reduce dimensionality and investigate which subset of variables should be more effective for classifying TIBO derivatives according to their degree of anti-HIV activity. The PCA showed that the EHOMO, μ , LogP , Q A, QB and MR variables are responsible for the separation between compounds with higher and lower anti-HIV activity. The HCA results are similar to those obtained with PCA. By using the che- mometric results, four synthetic compounds were analyzed through PCA and HCA and three of them are proposed as active molecules against HIV, which is consistent with the results of clinic experiments. The methodologies of PCA and HCA provide a reliable rule for classifying new TIBO derivatives with anti-HIV activity. The model obtained showed not only statistical significance but also predictive ability.

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.

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 of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives with AT1 receptor antagonistic activity

The quantitative structure-activity relationship(QSAR) of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives was studied.Three different alignment methods were used to get the models of the comparative molecular field analysis(CoMFA),the comparative molecular similarity indices analysis(CoMSIA),and the hologram quantitative structure?activity relationship(HQSAR).The statistical results from the established models show believable predictivity based on the cross-validated value(q2>0.5) and the non-validated value(r2>0.9),The analysis on contour maps of CoMFA and CoMSIA models suggests that hydrophobic and hydrogen-bond acceptor fields are important factors that affect the AT1 antagonistic activity of 2-alkyl-4-(biphenylylmethoxy) pyridine derivatives besides the steric and electrostatic fields,The structural modification information from different atom contributions in the HQSAR model is in agreement with that in the 3D-QSAR models.

Quantitative structure－activity study on the reductive dehalogenation potency of the halogenated aromatics

In this paper . the reductive dehalogenation potency of haloarenes in mathanogenic sedirnent - watersystems was divided into two classes according to the length of acclimation period and degradation rate con-stants during acclimation time. Based on the rn

Application of PCA and HCA to the Structure-Activity Relationship Study of Fluoroquinolones

用密度泛函理论计算了12种具有抗肺炎链球菌的氟诺喹酮类药物分子的分子特性.利用主成分分析和分层聚类分析方法来简化氟诺喹酮类药物数据库的维数.主成分分析法表明,变量 E_(LUMO)、 Q_3、Q_5、Q_A、lgP、MR、VOL、ΔE_(HL)能够有效地对抗肺炎链球菌的氟诺喹酮类药物进行分类.分层聚类方法的结果和主成分分析方法的结果一致.这表明两种方法都能够对新的具有抗肺炎链球菌的氟诺喹酮类药物的分类提供一个可信的规律.利用主成分分析法和分层聚类分析法对其他6个氟诺喹酮类药物分子进行分析,结果都表明有两个药物分子具有较强的抗肺炎链球菌活性.此结果和临床结果相吻合.

Molecular characteristics and structure–activity relationships of food-derived bioactive peptides

Peptides are functional active fragments of proteins which can provide nutrients needed for human growth and development,and they also have unique physiological activity characteristics relative to proteins.Bioactive peptides contain a great deal of development potential.More specifically,food-derived bioactive peptides have the advantages of a wide variety of sources,unique structures,high efficiency and safety,so they have broad development prospects.This review provides an overview of the current advances regarding the preparation,functional characteristics,and structure–activity relationships of food-derived bioactive peptides.Moreover,the prospects for the future development and application of food-derived bioactive peptides are discussed.This review may provide a better understanding of foodderived bioactive peptides,and some constructive inspirations for further research and applications in the food industry.

Determination of structure-activity relationships between fentanyl analogs and human μ-opioid receptors based on active binding site models

Fentanyl is a potent and widely used clinical narcotic analgesic,as well as a highly selective μ-opioid agonist.The present study established a homologous model of the human μ-opioid receptor;an intercomparison of three types of μ-opioid receptor protein sequence homologous rates was made.The secondary receptor structure was predicted,the model reliability was assessed and verified using the Ramachandran plot and ProTab analysis.The predictive ability of the CoMFA model was further validated using an external test set.Using the Surflex-Dock program,a series of fentanyl analog molecules were docked to the receptor,the calculation results from Biopolymer/SiteID showed that the receptor had a deep binding area situated in the extracellular side of the transmembrane domains(TM) among TM3,TM5,TM6,and TM7.Results suggested that there might be 5 active areas in the receptor.The important residues were Asp147,Tyr148,and Tyr149 in TM3,Trp293,and His297 in TM6,and Trp318,His319,Ile322,and Tyr326 in TM7,which were located at the 5 active areas.The best fentanyl docking orientation position was the piperidine ring,which was nearly perpendicular to the membrane surface in the 7 TM domains.Molecular dynamic simulations were applied to evaluate potential relationships between ligand conformation and fentanyl substitution.

Study on the Quantitative Structure-toxicity Relationships for the Selected Esters by Using Molecular Electronegativity Interaction Vector (MEIV)

The molecular electronegativity interaction vector (MEIV) was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression (MLR) and partial least-squares regression (PLS). The correlation coefficients (R) of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out (LOO) procedure. The cross-verification correlation coefficients (RCV) of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.

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 (χ), modified molecular connectivity indices ( mχth ) and valance molecular connectivity indices ( mχv ), with mχv 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.