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...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.展开更多
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 desc...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.展开更多
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 ...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.展开更多
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(Ab...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.展开更多
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 physic...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.展开更多
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...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.展开更多
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-act...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）.展开更多
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 we...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.展开更多
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-Acetoxyprogest...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.展开更多
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 c...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.展开更多
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 str...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.展开更多
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 gain...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.展开更多
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...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.展开更多
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 analy...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.展开更多
Estrogen compounds may pose a serious threat to the health of humans and wildlife. The estrogen receptor （ER） exists as two subtypes, ERα and ERβ. Compounds might have different relative affinities and binding mod...Estrogen compounds may pose a serious threat to the health of humans and wildlife. The estrogen receptor （ER） exists as two subtypes, ERα and ERβ. Compounds might have different relative affinities and binding modes for ERα and ERβ. In this study, the heuristic method was performed on 31 compounds binding to ERβ to select 5 variances most related to the activity （LogRBA） from 1524 variances, which were then employed to develop the best model with the significant correlation and the best predictive power （γ^2 = 0.829, q^2LOO = 0.742, γ^2pred = 0.772, q^2ext = 0.724, RMSEE = 0.395） using multiple linear regression （MLR）. The model derived identified critical structural features related to the activity of binding to ERβ. The applicability domain （AD） of the model was assessed by Williams plot.展开更多
Pythium stalk rot(PSR)is a destructive disease of maize,severely affecting yield and grain quality.The identification of quantitative trait loci(QTL)or genes for resistance to PSR forms the basis of diseaseresistant h...Pythium stalk rot(PSR)is a destructive disease of maize,severely affecting yield and grain quality.The identification of quantitative trait loci(QTL)or genes for resistance to PSR forms the basis of diseaseresistant hybrids breeding.In this study,a major QTL,Resistance to Pythium stalk rot 1(RPSR1),was identified from a set of recombinant inbred lines derived from MS71 and POP.Using a recombinant progeny testing strategy,RPSR1 was fine-mapped in a 472 kb interval.Through candidate gene expression,gene knock-down and knock-out studies,a leucine-rich repeat receptor-like kinase gene,PEP RECEPTOR 2(ZmPEPR2),was assigned as a PSR resistance gene.These results provide insights into the genetic architecture of resistance to PSR in maize,which should facilitate breeding maize for resistance to stalk rot.展开更多
All-vanadium flow batteries(VFBs)are one of the most promising large-scale energy storage technologies.Conducting an operando quantitative analysis of the polarizations in VFBs under different conditions is essential ...All-vanadium flow batteries(VFBs)are one of the most promising large-scale energy storage technologies.Conducting an operando quantitative analysis of the polarizations in VFBs under different conditions is essential for developing high power density batteries.Here,we employ an operando decoupling method to quantitatively analyze the polarizations in each electrochemical and chemical reaction of VFBs under different catalytic conditions.Results show that the reduction reaction of V^(3+)presents the largest activation polarization,while the reduction reaction of VO_(2)^(+)primarily contributes to concentration polarizations due to the formation of the intermediate product V_(2)O_(3)^(3+).Additionally,it is found that the widely used electrode catalytic methods,incorporating oxygen functional groups and electrodepositing Bi,not only enhance the reaction kinetics but also exacerbate concentration polarizations simultaneously,especially during the discharge process.Specifically,in the battery with the high oxygen-containing electrodes,the negative side still accounts for the majority of activation loss(75.3%)at 200 mA cm^(-2),but it comes down to 36,9% after catalyzing the negative reactions with bismuth.This work provides an effective way to probe the limiting steps in flow batteries under various working conditions and offers insights for effectively enhancing battery performance for future developments.展开更多
Supercritical CO_(2)(SC-CO_(2))fracturing stands out a promising waterless stimulation technique in the development of unconventional resources.While numerous studies have delved into the inducedfracture mechanism of ...Supercritical CO_(2)(SC-CO_(2))fracturing stands out a promising waterless stimulation technique in the development of unconventional resources.While numerous studies have delved into the inducedfracture mechanism of SC-CO_(2),the small scale of rock samples and synthetic materials used in many studies have limited a comprehensive understanding of fracture propagation in unconventional formations.In this study,cubic tight sandstone samples with dimensions of 300 mm were employed to conduct SC-CO_(2)fractu ring experiments under true-triaxial stre ss conditions.The spatial morphology and quantitative attributes of fracture induced by water and SC-CO_(2)fracturing were compared,while the impact of in-situ stress on fracture propagation was also investigated.The results indicate that the SCCO_(2)fracturing takes approximately ten times longer than water fracturing.Furthermore,under identical stress condition,the breakdown pressure(BP)for SC-CO_(2)fracturing is nearly 25%lower than that for water fracturing.A quantitative analysis of fracture morphology reveals that water fracturing typically produces relatively simple fracture pattern,with the primary fracture distribution predominantly controlled by bedding planes.In contrast,SC-CO_(2)fracturing results in a more complex fracture morphology.As the differential of horizontal principal stress increases,the BP for SC-CO_(2)fractured rock exhibits a downward trend,and the induced fracture morphology becomes more simplified.Moreover,the presence of abnormal in-situ stress leads to a further increase in the BP for SC-CO_(2)fracturing,simultaneously enhancing the development of a more conductive fracture network.These findings provide critical insights into the efficiency and behavior of SC-CO_(2)fracturing in comparison to traditional water-based fracturing,offering valuable implication for its potential applications in unconventional reservoirs.展开更多
Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological age.To predict bone age more accurately based on radiographs,and for the left-hand X-ray im...Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological age.To predict bone age more accurately based on radiographs,and for the left-hand X-ray images of different races model can have better adaptability,we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for BAA.In this study,a lightweight feature extractor(LFE)is designed to obtain the featuremaps fromradiographs,and amodule called attention erasermodule(AEM)is proposed to capture the fine-grained features.Meanwhile,the dimensional information of the metacarpal parts in the radiographs is measured to enhance the model’s generalization capability across images fromdifferent races.Ourmodel is trained and validated on the RSNA,RHPE,and digital hand atlas datasets,which include images from various racial groups.The model achieves a mean absolute error(MAE)of 4.42 months on the RSNA dataset and 15.98 months on the RHPE dataset.Compared to ResNet50,InceptionV3,and several state-of-the-art methods,our proposed method shows statistically significant improvements(p<0.05),with a reduction in MAE by 0.2±0.02 years across different racial datasets.Furthermore,t-tests on the features also confirm the statistical significance of our approach(p<0.05).展开更多
Quantitative detection of trace small-sized nanoplastics(<100 nm)remains a significant challenge in surface-enhanced Raman scattering(SERS).To tackle this issue,we developed a hydrophobic CuO@Ag nanowire substrate ...Quantitative detection of trace small-sized nanoplastics(<100 nm)remains a significant challenge in surface-enhanced Raman scattering(SERS).To tackle this issue,we developed a hydrophobic CuO@Ag nanowire substrate and introduced a multiplex-feature analysis strategy based on the coffee ring effect.This substrate not only offers high Raman enhancement but also exhibits a high probability of detection(POD),enabling rapid and accurate identification of 50 nm polystyrene nanoplastics over a broad concentration range(1–10−10 wt%).Importantly,experimental results reveal a strong correlation between the coffee ring formation and the concentration of nanoplastic dispersion.By incorporating Raman signal intensity,coffee ring diameter,and POD as combined features,we established a machine learning-based mapping between nanoplastic concentration and coffee ring characteristics,allowing precise predictions of dispersion concentration.The mean squared error of these predictions is remarkably low,ranging from 0.21 to 0.54,representing a 19 fold improvement in accuracy compared to traditional linear regression-based methods.This strategy effectively integrates SERS with wettability modification techniques,ensuring high sensitivity and fingerprinting capabilities,while addressing the limitations of Raman signal intensity in accurately reflecting concentration changes at ultra-low levels,providing a new idea for precise SERS measurements of nanoplastics.展开更多
基金This work was supported by the National Natural Science Foundation of China （No.21477121）, and the Fundamental Research Funds for the Central Universities for the support of this work. The numerical calculations were performed on the super computing system in the Supercomputing Center at the University of Science and Technology of China.
文摘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.
基金Project supported by the Natural Science Foundation of Shanghai, China(No. 06ZR14002).
文摘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.
基金Project(No.31071501)supported by the National Natural Science Foundation of China
文摘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.
基金Supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX23_0082)
文摘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.
基金Supported by the National High Technology Research and Development Program of China (863 Program, No. 2006AA02Z312)
文摘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.
基金supported by the Natural Science Foundation of Fujian Province (D0710019)the Natural Science Foundation of Overseas Chinese Affairs Office of the State Council (06QZR09)
文摘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.
基金Supported by the Chinese National Key Technologies R & D Program of 11th Five-year Plan (2006BAD27B06)Education Foundation of Innovative Engineering Key Project of Education Department (707034)
文摘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）.
基金Supported by the Chinese National Key Technologies R&D Program of 11th Five-year Plan (2006BAD27B06)the Fundamental Research Funds for the Central Universities and Education Foundation of Innovative Engineering Key Project of Education Department (707034)
文摘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.
基金Funded by Chongqing Medical University Scientific Research Foundation
文摘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.
基金This work was performed within the framework of the research project No 172017 supported by the Ministry of Education,Science and Technological development of Serbia.
文摘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.
基金supported by the National Natural Science Foundation of China(No.21472040)the Scientific Research Fund of Hunan Education Department(Nos.16A047 and 18A344)the Open Project Program of Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration(Hunan Institute of Engineering)(2018KF11)
文摘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.
文摘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.
文摘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.
基金The project was supported by the National Natural Science Foundation of China (No. 10574096)
文摘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.
基金supported by the Science and Technology Development Foundation Key Project of Nanjing Medical University (09NJMUZ16)
文摘Estrogen compounds may pose a serious threat to the health of humans and wildlife. The estrogen receptor （ER） exists as two subtypes, ERα and ERβ. Compounds might have different relative affinities and binding modes for ERα and ERβ. In this study, the heuristic method was performed on 31 compounds binding to ERβ to select 5 variances most related to the activity （LogRBA） from 1524 variances, which were then employed to develop the best model with the significant correlation and the best predictive power （γ^2 = 0.829, q^2LOO = 0.742, γ^2pred = 0.772, q^2ext = 0.724, RMSEE = 0.395） using multiple linear regression （MLR）. The model derived identified critical structural features related to the activity of binding to ERβ. The applicability domain （AD） of the model was assessed by Williams plot.
基金supported by National Natural Science Foundation of China(32302371 to Junbin Chen)the National Key Research and Development Program,Ministry of Science and Technology of China(2022YFD1201802 to Wangsheng Zhu)Research Program from State Key Laboratory of Maize Biobreeding(SKLMB2424 to Wangsheng Zhu).
文摘Pythium stalk rot(PSR)is a destructive disease of maize,severely affecting yield and grain quality.The identification of quantitative trait loci(QTL)or genes for resistance to PSR forms the basis of diseaseresistant hybrids breeding.In this study,a major QTL,Resistance to Pythium stalk rot 1(RPSR1),was identified from a set of recombinant inbred lines derived from MS71 and POP.Using a recombinant progeny testing strategy,RPSR1 was fine-mapped in a 472 kb interval.Through candidate gene expression,gene knock-down and knock-out studies,a leucine-rich repeat receptor-like kinase gene,PEP RECEPTOR 2(ZmPEPR2),was assigned as a PSR resistance gene.These results provide insights into the genetic architecture of resistance to PSR in maize,which should facilitate breeding maize for resistance to stalk rot.
基金supported by the Guangdong Major Project of Basic and Applied Basic Research(2023B0303000002)the National Natural Science Foundation of China(No.52206089)+3 种基金the Guangdong Basic and Applied Basic Research Foundation(2024A1515010288,2023B1515120005)the Natural Science Foundation of Shenzhen(JCYJ20230807093315033)the Shenzhen Engineering Research Center,Southern University of Science and Technology(No.XMHT20230208003)high level of special funds(G03034K001)。
文摘All-vanadium flow batteries(VFBs)are one of the most promising large-scale energy storage technologies.Conducting an operando quantitative analysis of the polarizations in VFBs under different conditions is essential for developing high power density batteries.Here,we employ an operando decoupling method to quantitatively analyze the polarizations in each electrochemical and chemical reaction of VFBs under different catalytic conditions.Results show that the reduction reaction of V^(3+)presents the largest activation polarization,while the reduction reaction of VO_(2)^(+)primarily contributes to concentration polarizations due to the formation of the intermediate product V_(2)O_(3)^(3+).Additionally,it is found that the widely used electrode catalytic methods,incorporating oxygen functional groups and electrodepositing Bi,not only enhance the reaction kinetics but also exacerbate concentration polarizations simultaneously,especially during the discharge process.Specifically,in the battery with the high oxygen-containing electrodes,the negative side still accounts for the majority of activation loss(75.3%)at 200 mA cm^(-2),but it comes down to 36,9% after catalyzing the negative reactions with bismuth.This work provides an effective way to probe the limiting steps in flow batteries under various working conditions and offers insights for effectively enhancing battery performance for future developments.
基金funded by the National Natural Scientific Foundation of China(Nos.52304008,52404038,52474043)the China Postdoctoral Science Foundation(No.2023MD734223)+1 种基金the Key Laboratory of Well Stability and Fluid&Rock Mechanics in Oil and Gas Reservoir of Shaanxi Province(No.23JS047)the Youth Talent Lifting Program of Xi'an Science and Technology Association(No.959202413078)。
文摘Supercritical CO_(2)(SC-CO_(2))fracturing stands out a promising waterless stimulation technique in the development of unconventional resources.While numerous studies have delved into the inducedfracture mechanism of SC-CO_(2),the small scale of rock samples and synthetic materials used in many studies have limited a comprehensive understanding of fracture propagation in unconventional formations.In this study,cubic tight sandstone samples with dimensions of 300 mm were employed to conduct SC-CO_(2)fractu ring experiments under true-triaxial stre ss conditions.The spatial morphology and quantitative attributes of fracture induced by water and SC-CO_(2)fracturing were compared,while the impact of in-situ stress on fracture propagation was also investigated.The results indicate that the SCCO_(2)fracturing takes approximately ten times longer than water fracturing.Furthermore,under identical stress condition,the breakdown pressure(BP)for SC-CO_(2)fracturing is nearly 25%lower than that for water fracturing.A quantitative analysis of fracture morphology reveals that water fracturing typically produces relatively simple fracture pattern,with the primary fracture distribution predominantly controlled by bedding planes.In contrast,SC-CO_(2)fracturing results in a more complex fracture morphology.As the differential of horizontal principal stress increases,the BP for SC-CO_(2)fractured rock exhibits a downward trend,and the induced fracture morphology becomes more simplified.Moreover,the presence of abnormal in-situ stress leads to a further increase in the BP for SC-CO_(2)fracturing,simultaneously enhancing the development of a more conductive fracture network.These findings provide critical insights into the efficiency and behavior of SC-CO_(2)fracturing in comparison to traditional water-based fracturing,offering valuable implication for its potential applications in unconventional reservoirs.
基金supported by the grant from the National Natural Science Foundation of China(No.72071019)grant from the Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0185).
文摘Bone age assessment(BAA)aims to determine whether a child’s growth and development are normal concerning their chronological age.To predict bone age more accurately based on radiographs,and for the left-hand X-ray images of different races model can have better adaptability,we propose a neural network in parallel with the quantitative features from the left-hand bone measurements for BAA.In this study,a lightweight feature extractor(LFE)is designed to obtain the featuremaps fromradiographs,and amodule called attention erasermodule(AEM)is proposed to capture the fine-grained features.Meanwhile,the dimensional information of the metacarpal parts in the radiographs is measured to enhance the model’s generalization capability across images fromdifferent races.Ourmodel is trained and validated on the RSNA,RHPE,and digital hand atlas datasets,which include images from various racial groups.The model achieves a mean absolute error(MAE)of 4.42 months on the RSNA dataset and 15.98 months on the RHPE dataset.Compared to ResNet50,InceptionV3,and several state-of-the-art methods,our proposed method shows statistically significant improvements(p<0.05),with a reduction in MAE by 0.2±0.02 years across different racial datasets.Furthermore,t-tests on the features also confirm the statistical significance of our approach(p<0.05).
基金the National Natural Science Foundation of China(No.12174229 and 22375117)Natural Science Foundation of Shandong Province(No.ZR2022YQ02 and ZR2023MB149)Taishan Scholars Program of Shandong Province(No.tsqn202306152)for financial support.
文摘Quantitative detection of trace small-sized nanoplastics(<100 nm)remains a significant challenge in surface-enhanced Raman scattering(SERS).To tackle this issue,we developed a hydrophobic CuO@Ag nanowire substrate and introduced a multiplex-feature analysis strategy based on the coffee ring effect.This substrate not only offers high Raman enhancement but also exhibits a high probability of detection(POD),enabling rapid and accurate identification of 50 nm polystyrene nanoplastics over a broad concentration range(1–10−10 wt%).Importantly,experimental results reveal a strong correlation between the coffee ring formation and the concentration of nanoplastic dispersion.By incorporating Raman signal intensity,coffee ring diameter,and POD as combined features,we established a machine learning-based mapping between nanoplastic concentration and coffee ring characteristics,allowing precise predictions of dispersion concentration.The mean squared error of these predictions is remarkably low,ranging from 0.21 to 0.54,representing a 19 fold improvement in accuracy compared to traditional linear regression-based methods.This strategy effectively integrates SERS with wettability modification techniques,ensuring high sensitivity and fingerprinting capabilities,while addressing the limitations of Raman signal intensity in accurately reflecting concentration changes at ultra-low levels,providing a new idea for precise SERS measurements of nanoplastics.
