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qsar Models for Predicting Additive and Synergistic Toxicities of Binary Pesticide Mixtures on Scenedesmus Obliquus

Chinese Journal of Structural Chemistry 2022年第3期41卷 166-177,I0011页

作者：莫凌云袁柏康朱杰覃礼堂代俊峰 College of Environmental Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology Guilin University of TechnologyGuilinGuangxi 541004China Technical Innovation Center of Mine Geological Environmental Restoration Engineering in Southern Karst Area MNRNanning 530023China Guangxi Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of TechnologyGuilin 541004China

Pesticides released into the environment may pose potential risks to the ecological system and hu-man health.However,existing toxicity data on pesticide mixtures still lack,especially regarding the toxic interac-tions of their mixtures.This study aimed to determine the toxic interactions of binary mixtures of pesticides on Scenedesmus Obliquus(S.obliquus)and to build quantitative structure-activity relationship models(QASR)for predicting the mixture toxicities.By applying direct equipartition ray method to design binary mixtures of five pes-ticides(linuron,dimethoate,dichlorvos,trichlorfon and metribuzin),the toxicity of a single pesticide and its mix-ture was tested by microplate toxicity analysis on S.obliquus.The QASR models were built for combined toxicity of binary mixtures of pesticides at the half-maximal effective concentration(EC_(50)),30%maximal effective concen-tration(EC_(30))and 10%maximal effective concentration(EC_(10)).The results showed that the single toxicity follows:metribuzin>linuron>dichlorvos>trichlorfon>dimethoate.The mixtures of linuron and trichlorfon,dichlorvos and metribuzin,dimethoate and metribuzin induced synergetic effects,while the remaining binary mixtures exhib-ited additive.The developed qsar models were internally validated using the leave-one-out cross-validation(LOO),leave-many-out cross-validation(LMO),bootstrapping,and y-randomization test,and externally validated by the test sets.All three qsar models satisfied well with the experimental values for all mixture toxicities,and presented high internally(R^(2)and Q^(2)>0.85)and externally(Q^(2)_(F1),Q^(2)_(F2),and Q^(2)_(F3)>0.80)predictive powers.The developed qsar models could accurately predict the toxicity values of EC_(50),EC_(30)and EC_(10)and were superior to the concentration addition model's results(CA).Compared to the additive effect,the qsar model could more ac-curately predict the binary mixture toxicities of pesticides with synergistic effects.

关键词： pesticide qsar toxicity prediction binary mixture algae

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Prediction of second-order rate constants between carbonate radical and organics by deep neural network combined with molecular fingerprints

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Chinese Chemical Letters 2022年第1期33卷 438-441页

作者： Peizhe Sun Huixin Ma Shangyu Li Hong Yao Ruochun Zhang School of Environmental Science and Engineering Tianjin UniversityTianjin 300072China School of Civil Engineering Tianjin UniversityTianjin 300072China Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard Department of Municipal and Environmental EngineeringSchool of Civil EngineeringBeijing Jiaotong UniversityBeijing 100044China Institute of Surface-Earth System Science School of Earth System ScienceTianjin UniversityTianjin 300072China Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim Tianjin UniversityTianjin 300072China

Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants(PCs).However,reaction rate constants between carbonate radical and most of the PCs have not been experimentally determined,and quantitative structural-activity relationships(qsars)have not been established for rate estimation.This study applied Max Min data processing method and used molecular fingerprints(MF)as the input of a deep neural network(DNN)to predict the rate constants between carbonate radical and organic compounds.MF parameters and the hyper-structure of the DNN were adjusted to yield satisfactory accuracy of rate prediction.The vector length of 512 bits with radius of 1 for MF and 5 hidden layers gave the best performance.The optimized MaxMin-MF-DNN model was compared with some of the most commonly used qsars and machine learning methods,including random data splitting,molecular descriptors,supporting vector machine,decision tree,etc.Results showed that the MF-DNN model out-performed the other methods by more than 10%increase in prediction accuracy.Applying this MF-DNN model,we estimated reaction rates between carbonate radical and pharmaceuticals used in human medicine(1576)and veterinary practice(390).Among them,46 drugs were identified as fast-reacting compounds,suggesting the important relations of their environmental fate with carbonate radical.

关键词： Deep neural network Carbonate radical Molecular fingerprints qsar Pharmaceuticals

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Environmental risk assessment of selected organic chemicals based on TOC test and qsar estimation models

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Journal of Environmental Sciences 2018年第2期30卷 23-31页

作者： Yulang Chi Huanteng Zhang Qiansheng Huang Yi Lin Guozhu Ye Huimin Zhu Sijun Dong Key Lab of Urban Environment and Health Institute of Urban Environment Chinese Academy of Sciences Xiamen 361021 China College of Resources and Environment University of Chinese Academy of Sciences Beijing 100049 China

Environmental risks of organic chemicals have been greatly determined by their persistence,bioaccumulation, and toxicity（PBT） and physicochemical properties. Major regulations in different countries and regions identify chemicals according to their bioconcentration factor（BCF） and octanol–water partition coefficient（Kow）, which frequently displays a substantial correlation with the sediment sorption coefficient（Koc）. Half-life or degradability is crucial for the persistence evaluation of chemicals. Quantitative structure activity relationship（qsar） estimation models are indispensable for predicting environmental fate and health effects in the absence of field-or laboratory-based data. In this study, 39 chemicals of high concern were chosen for half-life testing based on total organic carbon（TOC） degradation,and two widely accepted and highly used qsar estimation models（i.e., EPI Suite and PBT Profiler） were adopted for environmental risk evaluation. The experimental results and estimated data, as well as the two model-based results were compared, based on the water solubility, Kow, Koc, BCF and half-life. Environmental risk assessment of the selected compounds was achieved by combining experimental data and estimation models. It was concluded that both EPI Suite and PBT Profiler were fairly accurate in measuring the physicochemical properties and degradation half-lives for water, soil, and sediment.However, the half-lives between the experimental and the estimated results were still not absolutely consistent. This suggests deficiencies of the prediction models in some ways, and the necessity to combine the experimental data and predicted results for the evaluation of environmental fate and risks of pollutants.

关键词： qsar Risk assessment Predictive toxicology TOC Physicochemical property

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qsar Studies on 7-Substituted Fluoroquinolones

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Chinese Journal of Structural Chemistry 2007年第1期26卷 91-97页

作者：许菊丽高树林张向飞谢异萍黄晓因谢小光 Department of Chemistry Yunnan University Computer Science Department Yunnan Agricultural University

The PM3 and B3LYP methods were employed to calculate the properties of 18 7-substituted fluoroquinolones. The correlation between biological activity （against gram-positive organisms or gram-negative organisms） and structural properties was obtained by using multiple linear regression （MLR） methods： The best model generated correlates the antibacterial activity with EHOMO and QF8 for gram-positive organisms, and EHOMO and dipole moment for gram-negative organisms, respectively. It suggests that the interaction mechanisms ,of fluoroquinolons with gram-positive and gram-negative organisms are different.

关键词： qsar fluoroquinolones gram-positive organisms gram-negative organisms

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qsar Studies on 6-(1-Naphthylmethyl) Substituted S-DABO Derivatives as Novel Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors

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Chinese Journal of Structural Chemistry 2008年第10期27卷 1214-1222页

作者：殷丽琴余仕问姚凌峰何严萍谢小光 Department of Chemistry Yunnan University Key Laboratory of Medicinal Chemistry for Natural Resource(Yunnan University) Ministry of Education

The AM1 and B3LYP methods were employed to calculate the structural properties of 20 6-（1-naphthylmethyl） substituted S-DABO derivatives with β-carbonyl group on the C（2） side chain as novel potent non-nucleoside HIV-1 reverse transcriptase inhibitors. The correlation analysis （CA） and stepwise multiple regression analysis （SMR） were performed. The qsar models indicate that the physicochemical parameters of Qc9, MRR1, ELUMO, ∏R2 and μ have significant influence on the activities of these derivatives. The substitution of hydrophobic R2 and bulky aromatic R1 to form a conjugated system with the frame of those S-DABO series compounds should be considered to design new potent compounds for anti-HIV-1.

关键词： S-DABO derivatives correlation analysis stepwise regression analysis qsar LUMO

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qsar Study of Nitrobenzenes’ Toxicity to Tetrahymena Pyriformis Using Semi-empirical Quantum Chemical Methods

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Chinese Journal of Structural Chemistry 2007年第1期26卷 7-14页

作者：闫秀芬肖鹤鸣 Institute for Computation in Molecular and Material Science School of Chemical Engineering Nanjing University of Science and Technology

The molecular geometries and electronic structures of 30 nitrobenzenes have been calculated by using semi-empirical MO AM1 and PM3 methods. EHOMO, ELUMO, ENHOMO, ENLUMO, AE, QNO2, Qc and V were selected as the structural descriptors. The acute toxicity （-log/C50） of nitrobenzenes to tetrahymena pyriformis along with the above eight structural parameters was used to establish the quantitative structure-activity relationships （qsars）. The results indicate that the established model based on AM I method is superior to that on PM3 method not only for the stability but also for the predictive powers of the model. Based on AM1 parameters, a further classifying discussion was presented for the study of nitrobenzene toxic mechanism. The results show that the substituents, nitro group and halogen substituents on the aromatic ring are crucial to the chemicals＇ toxicity. For nitrobenzenes without halogen or other substituent, the reduction of nitro group is the main route. However, for those with halogen substituents, their next lowest unoccupied molecular orbital may take part in the toxic action betweeen the chemicals and macromolecules, and ENLUMO has the most important effect on these chemicals＇ toxicity.

关键词： nitrobenzenes toxicity qsar semi-empirical MO orbital methods

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qsar,docking studies and pharmacophore identification of phenylmethyl phenoxy propyl amino propanoic acid derivatives as leukotriene A_4 hydrolase inhibitors

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药学学报 2010年第5期45卷 615-623页

作者： SONAWANE Lalit V BARI Sanjaykumar B Drug Design and Development R. C. Patel Institute of Pharmaceutical Education and Research Karwand Naka Shirpur Dist. - Dhule Maharashtra State 425405 India

The enzyme leukotriene A4 (LTA4) plays an important role as precursor of slow reactive substances as LTC4, LTD4, and LTE4. It is an attractive target for molecular modeling and qsar study. Our effort is mainly focused on exploring the SAR for inhibitors of the LTA4 hydrolase through docking study, pharmacophore modeling and molecular descriptor study. The binding of these small molecules on LTA4 hydrolase enzyme was described by the models developed on 2D molecular descriptors, with good predictive power (39 compounds, 6 descriptors, r2 0.98, SEE 0.167, F-value 268.53, q2 0.90, r2adj 0.97, P-value qsar, docking and pharmacophore.

关键词： LTA4 hydrolase qsar molecular descriptor docking pharmacophore

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Support vector machine for SAR/qsar of phenethyl-amines

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Acta Pharmacologica Sinica 2007年第7期28卷 1075-1086页

作者： Bing NIU~2,Wen-cong LU~(2,3,6),Shan-sheng YANG~2,Yu-dong CAI~(3,4,6),Guo-zheng LI~5 ~2 College of Material Science and Engineering,Shanghai University,Shanghai 200444,China ~3 Laboratory of Chemical Data Mining,Department of Chemistry,College of Sciences,Shanghai University,Shanghai 200444,China ~4 Department of Combinatorics and Geometry,CAS-MPG Partner Institute for Computational Biology,Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences,Shanghai200031,China ~5 School of Computer Science and Engineering,Shanghai University,Shanghai 200072,China College of Material Science and Engineering Shanghai University Shanghai China Department of Chemistry Laboratory of Chemical Data Mining College of Sciences Shanghai University Shanghai China Department of Combinatorics and Geometry CAS-MPG Partner Institute for Computational Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China School of Computer Science and Engineering Shanghai Univeristy Shanghai China

Aim:To discriminate 32 phenethyl-amines between antagonists and agonists,and predict the activities of these compounds.Methods:The support vectormachine (SVM) is employed to investigate the structure-activity relationship(SAR)/quantitative structure-activity relationship (qsar) of phenethyl-aminesbased on molecular descriptors.Results:By using the leave-one-out cross-vali-dation (LOOCV) test,1 optimal SAR and 2 optimal qsar models for agonists andantagonists were attained.The accuracy of prediction for the classification ofphenethyl-amines by using the LOOCV test is 91.67%,and the accuracy of predic-tion for the classification of phenethyl-amines by using the independent test is100%;the results are better than those of the Fisher,the artificial neural network(ANN),and the K-nearest neighbor models for this real world data.The RMSE(root mean square error)of antagonists' qsar model is 0.5881,and the RMSE ofagonists' qsar model is 0.4779,which are better than those of the multiple linearregression,partial least squares,and ANN models for this real world data.Conclusion:The SVM can be used to investigate the SAR and qsar of phenethyl-amines and could be a promising tool in the field of SAR/qsar research.

关键词： support vector machine qsar phenethylamines antagonists agonists

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Quantitative structure-activity relationship in the photodegradation of azo dyes

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Journal of Environmental Sciences 2020年第4期32卷 41-50页

作者： Guoyang Zhang Shujuan Zhang State Key Laboratory of Pollution Control and Resource Reuse School of the EnvironmentNanjing UniversityNanjing 210023China

The photolysis characteristics of azo dyes are critically important in environmental pollution control,dye-sensitized solar cells,and dyeing-related industries.However,there is still lack of quantitative relationship between the structures of azo dyes and their photolysis characteristics.To address this issue,the photolysis of 22 azo dyes were conducted side by side at three pH(4.0,6.0,9.0).The obtained pseudo-first order photodegradation rate constants(k1)were processed with meta-analysis.Statistically,the hydrazone tautomer had a smaller excitation energy and was easier to undergo photolysis than the azo tautomer.The ortho-substituted sulfonate groups had an obvious protective effect on the photo stability of azo dyes.The softness(s),the most positive and negative partial charge on a carbon atom(qC+,qC-)were found to be crucial descriptors in the establishment of qsar models for the photo stability of azo dyes.The qsar model at pH 9.0 was robust for predicting the photo stability of azo dyes under UV irradiation.N2-purging experiments and quantum chemical computation verified that the cleavage of azo bond was not a result of direct photolysis but was caused by the attack of photoinduced reactive oxygen species.The results here are helpful for the design of more stable azo dyes or the selection of suitable approaches for the treatment of dyecontaminated water bodies.

关键词： Azo Hydrazone Photolysis qsar Softness

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Using electrotopological state indices to model the depuration rates of polychlorinated biphenyls in mussels of Elliptio complanata

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Journal of Environmental Sciences 2010年第10期22卷 1544-1550页

作者： Lei Wang Xinhui Liu Zhengjun Shan Lili Shi . Nanjing Institute of Environmental Sciences/Key laboratory of Pesticide Environmental Assessment and Pollution Control Ministry of Environmental Protection Nanjing 210042 China. State Key Laboratory of Water Environment Simulation School of Environment Beijing Normal University Beijing 100875 China

A quantitative structure-activity relationship （qsar） model on depuration rate constants （ka） of polychlorinated biphenyls （PCBs） in freshwater mussel Elliptio complanata was successfully constructed using electrotopological state indices （ESI） and partial least squares （PLS） regression. The cross validated 2 Qcum and the correlation coefficient R for the model were determined to be 0.845 and 0.954, respectively. The satisfactory Q2cum and R values indicated significantly high robustness and good predictive ability for the model. The model was tested and found acceptable for the prediction of logkd （the logarithm of the deputation rate constants） by validation set. According to the model, an increase in the values of Saasc, S5＇, S4, S5 and S4＇, led to increased logkd, and a decrease in the values of Na, S2, and S6 also resulted in increased logkd. Among these descriptors, Ncl, S aasC, S5＇,, S4 and S5 made significant contributions to the value of logkd. These significant descriptors showed that the depuration of PCBs in Elliptio complanata may be mainly attributed to an equilibrium partitioning process among compartments with different lipid contents, while the reactivity of PCBs with enzymes or other molecules may play a subordinate role.

关键词： deputation polychlorinated biphenyls electrotopological state indices qsar Elliptio complanata

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