Quantitative Structure-activity Relationship of Toxicity of Alkyl(1-phenylsulfonyl) Cycloalkane-carboxylates Using MLSER Model and Ab initio

Wang, Zunyao; Zhai, Zhicai; Wang, Liansheng

doi:10.1002/qsar.200430873

Cited by 19 publications

(7 citation statements)

References 25 publications

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“…It is partly because the ab initio method used in this study computes all electronic integrals without using any experiential parameter, which overcomes the limitations of the semi-empirical molecular orbital algorithm that comes from the neglect of the most repulsive interaction of electrons to reduce the calculation load and time. Rapid advancement of modern computational capability and development of fast algorithms allow the ab initio method to be expeditiously applied in current QSAR studies [42,43].…”

Section: Analysis and Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationships for Enzymatic Activity of Chloroperoxidase on Metabolizing Organophosphorus Pesticides

Dang

Tao

et al. 2007

QSAR Comb. Sci.

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Quantitative Structure -Activity Relationship (QSAR) is a powerful approach for predicting activities of organic pollutants with their structure descriptors. This study reports QSAR models for predicting enzymatic activity of chloroperoxidase on metabolizing selected Organophosphorus Pesticides (OPPs). We used ten quantum chemical descriptors computed with ab initio method at HF/6-31G(d) level and Partial Least Squares (PLS) analysis with optimizing procedure for generating QSAR models. The correlation coefficient of the optimal model was 0.918, and the fitting results showed that it had high fitting precision and good predicting ability. The PLS assistant analysis indicated that the atomic charges of sulfur and phosphorus atoms in the S ¼ P bond of an OPP molecule were important in governing the enzymatic activity and the molecular dipole moment also had some effect on the enzymatic activity. It was found that OPPs with high absolute values of atomic charges on the sulfur and phosphorus atoms tended to be metabolized faster, whereas OPPs with stronger polarity tended to be metabolized slower by chloroperoxidase. The optimal model was used to predict the enzymatic activity of two OPPs and the results appeared to be reasonable.

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Section: Analysis and Discussionmentioning

confidence: 99%

Quantitative Structure–Activity Relationships for Enzymatic Activity of Chloroperoxidase on Metabolizing Organophosphorus Pesticides

Dang

Tao

et al. 2007

QSAR Comb. Sci.

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“…Hence, the development of QSPR models in which quantum chemical descriptors are used is of great importance [32,33]. Rapid advancement of modern computational capability and development of fast algorithms allow the high precision method to be expeditiously applied in current QSPR studies [16,25,26,[34][35][36][37][38], several of which are about partitioning properties of environmental pollutants [16,25,26]. Modern theoretical method in quantum chemistry with high calculation precision was proved having its advantages in estimating properties of environmental concern [16,35].…”

Section: Introductionmentioning

confidence: 99%

Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

Tao

Dang

et al. 2008

Open Chemistry

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Quantitative structure-property relationship (QSPR) modeling is a powerful approach for predicting environmental behavior of organic pollutants with their structure descriptors. This study reports an optimal QSPR model for estimating logarithmic n-octanol/water partition coefficients (log K OW ) of polycyclic aromatic hydrocarbons (PAHs). Quantum chemical descriptors computed with density functional theory at B3LYP/6-31G(d) level and partial least squares (PLS) analysis with optimizing procedure were used for generating QSPR models for log K OW of PAHs. The squared correlation coefficient (R 2 ) of the optimal model was 0.990, and the results of crossvalidation test (Q 2 cum =0.976) showed this optimal model had high fitting precision and good predictability. The log K OW values predicted by the optimal model are very close to those observed. The PLS analysis indicated that PAHs with larger electronic spatial extent and lower total energy values tend to be more hydrophobic and lipophilic.© Versita Warsaw and Springer-Verlag Berlin Heidelberg.

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“…However, it is still a big challenge to develop a quantum‐chemical descriptor describing the bulk effects more directly and effectively. Recently, Wang et al22 and Han et al23 have tried to use the volume of electron cloud of a whole molecule as steric descriptor, but no satisfactory QSAR model containing this descriptor could be obtained. Keeping in mind that those activities of a series of analogues changed mainly due to different substitution, the volume of electron cloud of a specific substituent may be a preferable quantum‐chemical descriptor for directly describing the bulk effects of substituents.…”

Section: Introductionmentioning

confidence: 99%

Development of a general quantum‐chemical descriptor for steric effects: Density functional theory based QSAR study of herbicidal sulfonylurea analogues

Niu

et al. 2006

J Comput Chem

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Quantitative structure-activity relationship (QSAR) analysis has become one of the most effective approaches for optimizing lead compounds and designing new drugs. Although large number of quantum-chemical descriptors were defined and applied successfully, it is still a big challenge to develop a general quantum-chemical descriptor describing the bulk effects more directly and effectively. In this article, we defined a general quantum-chemical descriptor by characterizing the volume of electron cloud for specific substituent using the method of density functional theory. The application of our defined steric descriptors in the QSAR analysis of sulfonylurea analogues resulted in four QSAR models with high quality (the best model: q2 = 0.881, r2 = 0.901, n = 35, s = 0.401, F = 68.44), which indicated that this descriptor may provide an effective way for solving the problem how to directly describe steric effect in quantum chemistry-based QSAR studies.

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Quantitative Structure-activity Relationship of Toxicity of Alkyl(1-phenylsulfonyl) Cycloalkane-carboxylates Using MLSER Model and Ab initio

Cited by 19 publications

References 25 publications

Quantitative Structure–Activity Relationships for Enzymatic Activity of Chloroperoxidase on Metabolizing Organophosphorus Pesticides

Quantitative Structure–Activity Relationships for Enzymatic Activity of Chloroperoxidase on Metabolizing Organophosphorus Pesticides

Estimation of n-octanol/water partition coefficients of polycyclic aromatic hydrocarbons by quantum chemical descriptors

Development of a general quantum‐chemical descriptor for steric effects: Density functional theory based QSAR study of herbicidal sulfonylurea analogues

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