Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values c...Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values causing 50% inhibition of bacteria growth （24h-IC50） were determined according to the bacterial growth inhibition test method. The energy of the lowest unoccupied molecular orbital and the net charge of carbon atom of 20 halogenated benzenes were calculated by the quantum chemical MOPAC program. Results The logl/IC50 values ranged from 4.79 for 2,4-dinitrochlorobenzene to 3.65 for chlorobenzene. A quantitative structure-activity relationship model was derived from the toxicity and structural parameters： logl/IC50 =-0.531（ELUMO）＋1.693（Qc）＋0.163（logP）＋3.375. This equation was found to fit well （r^2=0.860, s=0.106）, and the average percentage error was only 1.98%. Conclusion Halogenated benzenes and alkyl halogenated benzenes are non-polar narcotics, and have hydrophobicity-dependent toxicity. The halogenated phenols and anilines exhibit a higher toxic potency than their hydrophobicity, whereas 2,4-dinitrochlorobenzene is electrophile with the halogen acting as the leaving group.展开更多
The two-dimensional Quantitative Structure-Activity Relationship （2D-QSAR） models have been developed to estimate and predict the inhibitory activities of a series of HEPT analogues against HIV-1 by using quantum ch...The two-dimensional Quantitative Structure-Activity Relationship （2D-QSAR） models have been developed to estimate and predict the inhibitory activities of a series of HEPT analogues against HIV-1 by using quantum chemical parameters and physicochemical parameters. The best model of three parameters yields r = 0.908, r^2A = 0.800 and s = 0.467 based on stepwise multiple regression （SMR） method. The stability of the model has been verified by t-test, and the results show that the model has perfect robustness. The predictive power of QSAR models has been tested by Leave-One-Out （LOO） and Leave-Group（regularly random set）-Out（LGO） procedure Cross-Validation methodology. The r^2cv of 0.755 and r^2pred of 0.759 were obtained, respectively.展开更多
Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyan...Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.展开更多
The molecular electronegativity-distance vector(MEDV)is employed todescribe the chemical structure of organic pollutants.Quantitative linear relationships between themolecular descriptors and BCF values are developed ...The molecular electronegativity-distance vector(MEDV)is employed todescribe the chemical structure of organic pollutants.Quantitative linear relationships between themolecular descriptors and BCF values are developed by best subset regression and partial leastsquare regression analysis.The main structural factors influencing the bioactivities are-CH2,-X,-Cnot【,-C not【,-O-.The high values of r2 and q2_(LOO)present good estimation ability and stabilityof models.The prediction power for external samples is validated by the model developed from thetraining set.展开更多
Nanoplastics toxicity has been framed as an emerging,distinct research area,purportedly addressing a new threat.While this focus has heightened public awareness and influenced the regulation of plastics,isolating nano...Nanoplastics toxicity has been framed as an emerging,distinct research area,purportedly addressing a new threat.While this focus has heightened public awareness and influenced the regulation of plastics,isolating nanoplastics toxicity risks inefficiently allocating research resources and hindering sustainable management strategies.Here,using data mining and machine learning,we show that research on nanoplastics toxicity closely mirrors that of engineered nanoparticles,a well-established domain of nanotoxicology.Examining 154,745 research articles on nanoparticle and nanoplastics toxicology,we find that both particle types share similar physicochemical properties,biological uptake mechanisms,toxicity profiles,and structure−toxicity relationships.Although nanoplastics pollution is more pervasive in scale and morphological diversity,its toxicological attributes align with those documented for other nanoscale materials.We challenge the notion that nanoplastics pose a distinct,separate risk,proposing instead that integrating nanoplastics toxicity into the broader field of nanotoxicology can streamline research,prevent duplication of effort,and more efficiently guide policies,resource use,and remediation strategies toward globally sustainable outcomes.展开更多
Phytophthora infestans control is a long-standing problem that has caused ongoing difficulties and brought limited success for over a century.Traditional methods,such as fungicides,have drawbacks including high cost,r...Phytophthora infestans control is a long-standing problem that has caused ongoing difficulties and brought limited success for over a century.Traditional methods,such as fungicides,have drawbacks including high cost,restrictions on organic farming,potential risks to the environment and human health,and the development of resistant strains.In this study,we employed cutting-edge computer-based techniques,including Quantitative Structure-Activity Relationship(QSAR)modeling and molecular docking simulations,to uncover new fungicidal compounds and gain insights into their specific mechanisms of action against P.infestans.QSAR modeling on the number of compounds tested as P.infestans inhibitors was performed using an interactive OCHEM web platform.The predictive ability of the developed classification models had a balanced accuracy(BA)of 77–85%for the training set and BA?89–93%for the validation external test set.During the in vitro testing against P.infestans,thirteen synthesized 2-oxoimidazolidine-4-sulfonamides demonstrated inhibition rates,ranging from 23.6%to 87.4%.The fungicidal potential of six of these fungicides ranged from 79.3%to 87.4%,which is comparable to the activity of known fungicides.Acute toxicity results using the well-known aquatic marker Daphnia magna showed that the most active sulfonamides 3d,3f,3h,3j,3k,and 3l,with LC_(50) values ranging from 13.7 to 52.9 mg/L,are low-toxicity compounds.The molecular docking results demonstrated a potential mechanism of the antifungal action of the studied 2-oxoimidazolidin-4-sulfonamide derivatives via the inhibition of fungal CYP51,a sterol biosynthesis enzyme.展开更多
The contamination of water resources by phenolic compounds(PCs)presents a significant environmental hazard,necessitating the development of novel materials and methodologies for effective mitigation.In this study,a me...The contamination of water resources by phenolic compounds(PCs)presents a significant environmental hazard,necessitating the development of novel materials and methodologies for effective mitigation.In this study,a metallic copper-doped zeolitic imidazolate framework was pyrolyzed and designated as CuNC-20 for the activation of peroxymonosulfate(PMS)to degrade phenol(PE).Cu-NC-20 could effectively address the issue of metal agglomeration while simultaneously diminishing copper dissolution during the activation of PMS reactions.The Cu-NC-20 catalyst exhibited a rapid degradation rate for PE across a broad pH range(3-9)and demonstrated high tolerance towards coexisting ions.According to scavenger experiments and electron paramagnetic resonance analysis,singlet oxygen(^(1)O_(2))and high-valent copperoxo(Cu(Ⅲ))were the predominant reactive oxygen species,indicating that the system was nonradicaldominated during the degradation process.The quantitative structure-activity relationship(QSAR)between the oxidation rate constants of various substituted phenols and Hammett constants was established.It indicated that the Cu-NC-20/PMS system had the optimal oxidation rate constant withσ^(-)correlation and exhibited a typical electrophilic reaction pattern.This study provides a comprehensive understanding of the heterogeneous activation process for the selective removal of phenolic compounds.展开更多
From a quantum chemistry standpoint,the impact of the structural properties of the compounds on activated carbon’s adsorption ability was specifically investigated.The compounds whose adsorption behavior followed the...From a quantum chemistry standpoint,the impact of the structural properties of the compounds on activated carbon’s adsorption ability was specifically investigated.The compounds whose adsorption behavior followed the Langmuir isotherm model were selected as the research objects.An optimal quantitative structure-activity relationship(QSAR)model was built by using the multiple linear regression(MLR)method,with the saturation adsorption capacity Q_(m) from the Langmuir adsorption isotherm as the response variable and the structural parameters of 50 organic compounds as independent variables.The results show that the optimal model exhibits good stability,reliability and robustness,with a regression coefficient R^(2)of 0.88,an adjusted regression coefficient R_(adj)^(2) of 0.87,an internal validation coefficient q^(2) of 0.81,and an external validation coefficient Q_(ext)^(2) of 0.68.The variables included in the optimal model indicate that the polarity of the molecule,the molecular potential energy,and the stability and bonding strength of the organic compound are the main factors affecting the adsorption on activated carbon.The results provide key information for predicting the adsorption capacity of organic compounds on activated carbon and offer a theoretical reference for adsorption treatment in water environments.展开更多
基金This work was supported by the National 973 Great Foundation Research Item of China (2002CB412303) and the National Natural Science Foundation of Jiangsu Province (BK2004118).
文摘Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values causing 50% inhibition of bacteria growth （24h-IC50） were determined according to the bacterial growth inhibition test method. The energy of the lowest unoccupied molecular orbital and the net charge of carbon atom of 20 halogenated benzenes were calculated by the quantum chemical MOPAC program. Results The logl/IC50 values ranged from 4.79 for 2,4-dinitrochlorobenzene to 3.65 for chlorobenzene. A quantitative structure-activity relationship model was derived from the toxicity and structural parameters： logl/IC50 =-0.531（ELUMO）＋1.693（Qc）＋0.163（logP）＋3.375. This equation was found to fit well （r^2=0.860, s=0.106）, and the average percentage error was only 1.98%. Conclusion Halogenated benzenes and alkyl halogenated benzenes are non-polar narcotics, and have hydrophobicity-dependent toxicity. The halogenated phenols and anilines exhibit a higher toxic potency than their hydrophobicity, whereas 2,4-dinitrochlorobenzene is electrophile with the halogen acting as the leaving group.
文摘The two-dimensional Quantitative Structure-Activity Relationship （2D-QSAR） models have been developed to estimate and predict the inhibitory activities of a series of HEPT analogues against HIV-1 by using quantum chemical parameters and physicochemical parameters. The best model of three parameters yields r = 0.908, r^2A = 0.800 and s = 0.467 based on stepwise multiple regression （SMR） method. The stability of the model has been verified by t-test, and the results show that the model has perfect robustness. The predictive power of QSAR models has been tested by Leave-One-Out （LOO） and Leave-Group（regularly random set）-Out（LGO） procedure Cross-Validation methodology. The r^2cv of 0.755 and r^2pred of 0.759 were obtained, respectively.
文摘Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.
基金Supported by the Natural Science Foundation of the Education Commission of Jiangsu Province(Grant No.07KJB610061)the National Natural Science Founda-tion of China(Grant No.20577023)+1 种基金the"973"Program(Grant No.2003CB415002)the"863"Program(Grant No.2001AA640601-4)
文摘The molecular electronegativity-distance vector(MEDV)is employed todescribe the chemical structure of organic pollutants.Quantitative linear relationships between themolecular descriptors and BCF values are developed by best subset regression and partial leastsquare regression analysis.The main structural factors influencing the bioactivities are-CH2,-X,-Cnot【,-C not【,-O-.The high values of r2 and q2_(LOO)present good estimation ability and stabilityof models.The prediction power for external samples is validated by the model developed from thetraining set.
基金funded by the National Natural Science Foundation of China(22036002,22106025,and 22476056)the introduced innovative R&D team project under the“The Pearl River Talent Recruitment Program”of Guangdong Province(2019ZT08L387)+1 种基金the National Basic Research Program of China(2022 YFC 3701301 and SQ2023YFA1700109)the Specific University Discipline Construction Project(2023B10564001).
文摘Nanoplastics toxicity has been framed as an emerging,distinct research area,purportedly addressing a new threat.While this focus has heightened public awareness and influenced the regulation of plastics,isolating nanoplastics toxicity risks inefficiently allocating research resources and hindering sustainable management strategies.Here,using data mining and machine learning,we show that research on nanoplastics toxicity closely mirrors that of engineered nanoparticles,a well-established domain of nanotoxicology.Examining 154,745 research articles on nanoparticle and nanoplastics toxicology,we find that both particle types share similar physicochemical properties,biological uptake mechanisms,toxicity profiles,and structure−toxicity relationships.Although nanoplastics pollution is more pervasive in scale and morphological diversity,its toxicological attributes align with those documented for other nanoscale materials.We challenge the notion that nanoplastics pose a distinct,separate risk,proposing instead that integrating nanoplastics toxicity into the broader field of nanotoxicology can streamline research,prevent duplication of effort,and more efficiently guide policies,resource use,and remediation strategies toward globally sustainable outcomes.
基金supported by the BASF Open Innovative Platform(Project OI39).
文摘Phytophthora infestans control is a long-standing problem that has caused ongoing difficulties and brought limited success for over a century.Traditional methods,such as fungicides,have drawbacks including high cost,restrictions on organic farming,potential risks to the environment and human health,and the development of resistant strains.In this study,we employed cutting-edge computer-based techniques,including Quantitative Structure-Activity Relationship(QSAR)modeling and molecular docking simulations,to uncover new fungicidal compounds and gain insights into their specific mechanisms of action against P.infestans.QSAR modeling on the number of compounds tested as P.infestans inhibitors was performed using an interactive OCHEM web platform.The predictive ability of the developed classification models had a balanced accuracy(BA)of 77–85%for the training set and BA?89–93%for the validation external test set.During the in vitro testing against P.infestans,thirteen synthesized 2-oxoimidazolidine-4-sulfonamides demonstrated inhibition rates,ranging from 23.6%to 87.4%.The fungicidal potential of six of these fungicides ranged from 79.3%to 87.4%,which is comparable to the activity of known fungicides.Acute toxicity results using the well-known aquatic marker Daphnia magna showed that the most active sulfonamides 3d,3f,3h,3j,3k,and 3l,with LC_(50) values ranging from 13.7 to 52.9 mg/L,are low-toxicity compounds.The molecular docking results demonstrated a potential mechanism of the antifungal action of the studied 2-oxoimidazolidin-4-sulfonamide derivatives via the inhibition of fungal CYP51,a sterol biosynthesis enzyme.
基金the financial support from Sichuan Program of Science and Technology(No.2021ZDZX0012)the National Natural Science Foundation of China(No.52200105)。
文摘The contamination of water resources by phenolic compounds(PCs)presents a significant environmental hazard,necessitating the development of novel materials and methodologies for effective mitigation.In this study,a metallic copper-doped zeolitic imidazolate framework was pyrolyzed and designated as CuNC-20 for the activation of peroxymonosulfate(PMS)to degrade phenol(PE).Cu-NC-20 could effectively address the issue of metal agglomeration while simultaneously diminishing copper dissolution during the activation of PMS reactions.The Cu-NC-20 catalyst exhibited a rapid degradation rate for PE across a broad pH range(3-9)and demonstrated high tolerance towards coexisting ions.According to scavenger experiments and electron paramagnetic resonance analysis,singlet oxygen(^(1)O_(2))and high-valent copperoxo(Cu(Ⅲ))were the predominant reactive oxygen species,indicating that the system was nonradicaldominated during the degradation process.The quantitative structure-activity relationship(QSAR)between the oxidation rate constants of various substituted phenols and Hammett constants was established.It indicated that the Cu-NC-20/PMS system had the optimal oxidation rate constant withσ^(-)correlation and exhibited a typical electrophilic reaction pattern.This study provides a comprehensive understanding of the heterogeneous activation process for the selective removal of phenolic compounds.
基金National Natural Science Foundation of China(No.21876025)National Key R&D Program of China(No.2023YFC3207204)Shanghai Municipal Education Commission Artificial Intelligence-Enabled Scientific Research Plan,China(No.SMEC-AI-DHUZ-07)。
文摘From a quantum chemistry standpoint,the impact of the structural properties of the compounds on activated carbon’s adsorption ability was specifically investigated.The compounds whose adsorption behavior followed the Langmuir isotherm model were selected as the research objects.An optimal quantitative structure-activity relationship(QSAR)model was built by using the multiple linear regression(MLR)method,with the saturation adsorption capacity Q_(m) from the Langmuir adsorption isotherm as the response variable and the structural parameters of 50 organic compounds as independent variables.The results show that the optimal model exhibits good stability,reliability and robustness,with a regression coefficient R^(2)of 0.88,an adjusted regression coefficient R_(adj)^(2) of 0.87,an internal validation coefficient q^(2) of 0.81,and an external validation coefficient Q_(ext)^(2) of 0.68.The variables included in the optimal model indicate that the polarity of the molecule,the molecular potential energy,and the stability and bonding strength of the organic compound are the main factors affecting the adsorption on activated carbon.The results provide key information for predicting the adsorption capacity of organic compounds on activated carbon and offer a theoretical reference for adsorption treatment in water environments.
