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.

Elucidating the structure-activity relationship of Cu-Ag bimetallic catalysts for electrochemical CO_(2) reduction

Developing bimetallic catalysts is an effective strategy for enhancing the activity and selectivity of electrochemical CO_(2) reduction reactions,where understanding the structure-activity relationship is essential for catalyst design.Herein,we prepared two Cu-Ag bimetallic catalysts with Ag nanoparticles attached to the top or the bottom of Cu nanowires.When tested in a flow cell,the Cu-Ag catalyst with Ag nanoparticles on the bottom achieved a faradaic efficiency of 54%for ethylene production,much higher than the catalyst with Ag nanoparticles on the top.The catalysts were further studied in the H-cell and zero-gap MEA cell.It was found that placing the two metals in the intensified reaction zone is crucial to triggering the tandem reaction of bimetallic catalysts.Our work elucidates the structure-activity relationship of bimetallic catalysts for CO_(2) reduction and demonstrates the importance of considering both catalyst structures and cell characteristics to achieve high activity and selectivity.

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.

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.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

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.

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 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(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 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.

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, QA, 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 chemometric 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.

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.

Review on mechanisms and structure-activity relationship of hypoglycemic effects of polysaccharides from natural resources

Diabetes mellitus(DM)is a common multifactorial disease,causing various complications,such as chronic metabolism.The current therapies for diabetes mellitus are commercial diabetic drugs that have different definite side effect.However,polysaccharides mainly extracted from natural resources,have advantages of safety,accessibility,and anti-diabetic potential.We have summarized recent research of natural polysaccharides with hypoglycemic activities,focusing on different pharmacological mechanisms in various cell and animal models.The relationships of structure-hypoglycemic effect are also discussed in detail.This review could provide a comprehensive perspective for better understanding on development and mechanism of natural polysaccharides against diabetes mellitus,which have been required by clinical studies yet.

Advances in Research of Biological Activity,Action Mechanism and Structure-Activity Relationship of Lentinan

Lentinula edodes is the second largest edible mushroom in the world and is widely used as food and medicine.Modern research shows that lentinan(LNT)is the main active component of L.edodes.It has anti-cancer,treatment of diabetes,intestinal protection,anti-inflammatory,anti-oxidation,anti-aging,hepatoprotective,immune-regulating effects.In this review,the biological activity,action mechanism and structure-activity relationship of LNT in recent years are reviewed.On this basis,the existing problems were discussed,and the future research and application of LNT were prospected.Finally,it is hoped that this review will promote the in-depth study of LNT and provide a reference for its development as a drug and functional food.

Quantitative relationship between surface sedimentary diatoms and water depth in North-Central Bohai Bay,China

To study the quantitative relationship between surface sedimentary diatoms and water depth,67 surface samples were collected for diatom analysis on eight profiles with water depth variation from the muddy intertidal zone to the shallow sea area in North-Central Bohai Bay,China.The results showed that the distribution of diatoms changed significantly in response to the change in water depth.Furthermore,the quantitative relationship between the distribution of dominant diatom species,their assemblages,and the water depth was established.The water depth optima for seven dominant species such as Cyclotella striata/stylorum,Paralia sulcata,and Coscinodiscus perforatus and the water depth indication range of seven diatom assemblages were obtained in the study area above the water depth(elevation)of-10 m.The quantitative relationship between surface sedimentary diatoms and water depth provides a proxy index for diatom-paleo-water depth reconstruction in the strata in Bohai Bay,China.

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.

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 IJ-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/SitelD showed that the receptor had a deep binding area situated in the extracellular side of the transmembrane domains （TM） among TM3, TM5, TM6, and TMT. 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.