Modeling the toxicity of amide herbicides using the electrotopological state

Gough, Jonathan D.; Hall, Lowell H.

doi:10.1002/etc.5620180535

Cited by 37 publications

(22 citation statements)

References 20 publications

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“…Several studies on toxicity, including benzene toxicity (Hall et al, 1989a,b), phenol toxicity (Hall and Vaughn, 1997), toxicity to fish (Rose and Hall, 2003) and amide herbicide toxicity (Gough and Hall, 1999), have employed only topological indices in modeling structure--activity relationships (QSARs), as is done in this present study. Because calculated logP has been shown to be a useful parameter in some previous The Z-score value regulates the size of the model applicability domain, therefore, a smaller value forces the model to only predict test compounds with high similarity to those in the training set.…”

Section: Discussionmentioning

confidence: 99%

Three new consensus QSAR models for the prediction of Ames genotoxicity

Votano¹

2004

Mutagenesis

148

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Three QSAR methods, artificial neural net (ANN), k-nearest neighbors (kNN), and Decision Forest (DF), were applied to 3363 diverse compounds tested for their Ames genotoxicity. The ratio of mutagens to non-mutagens was 60/40 for this dataset. This group of compounds includes >300 therapeutic drugs. All models were developed using the same initial set of 148 topological indices: molecular connectivity chi indices and electrotopological state indices (atom-type, bond-type and group-type E-state), as well as binary indicators. While previous studies have found logP to be a determining factor in genotoxicity, it was not found to be important by any modeling method employed in this study. The three models yielded an average training/test concordance value of 88%, with a low percentage of false positives and false negatives. External validation testing on 400 compounds not used for QSAR model development gave an average concordance of 82%. This value increased to 92% upon removal of less reliable outcomes, as determined by a reliability criterion used within each model. The ANN model showed the best performance in predicting drug compounds, yielding 97% concordance (34/35 drugs) after the removal of less reliable predictions. The appreciable commonality found among the top 10 ranked descriptors from each model is of particular interest because of the diversity in the learning algorithms and descriptor selection techniques employed in this study. Forty percent of the most important descriptors in any one model are found in one or two other models. Fourteen of the most important descriptors relate directly to known toxicophores involved in potent genotoxic responses in Salmonella typhimurium. A comparison of the validation results with those of MULTICASE and DEREK indicated that the new models presented in this work perform substantially better than the former models in predicting genotoxicity of therapeutic drugs. Substantially higher specificity was achieved with these new models as compared with MULTICASE or DEREK with comparable sensitivities among all models.

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Section: Discussionmentioning

confidence: 99%

Three new consensus QSAR models for the prediction of Ames genotoxicity

Votano¹

2004

Mutagenesis

148

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“…An important objective of property modeling is obtaining useful information about the structure features that relate significantly to the property being modeled, in addition to speed and cost considerations. For this present case, we use the molecular structure descriptors known as electrotopological state indices (E-State) − ,− along with molecular connectivity chi indices. − The E-State indices have been used to develop models for many activities and properties in both their atom-level 3-5 and atom-type forms. ,− ,, E-State QSAR models yield structure information that reveal structure features significantly related to activity. Further, the more recent development of hydrogen E-State values (and hydrogen atom-type E-State indices 3,19,24 ) has extended the capability of the E-State as a powerful set of structure descriptors.…”

Section: Introductionmentioning

confidence: 99%

Modeling Blood-Brain Barrier Partitioning Using the Electrotopological State

Rose

Hall

Kier

2002

J. Chem. Inf. Comput. Sci.

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162

114

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The challenging problem of modeling blood-brain barrier partitioning is approached through topological representation of molecular structure. A QSAR model is developed for in vivo blood-brain partitioning data treated as the logarithm of the blood-brain concentration ratio. The model consists of three structure descriptors: the hydrogen E-State index for hydrogen bond donors, HS(T)(HBd); the hydrogen E-State index for aromatic CHs, HS(T)(arom); and the second order difference valence molecular connectivity index, d(2)chi(v) (q(2) = 0.62.) The model for the set of 106 compounds is validated through use of an external validation test set (20 compounds of the 106, MAE = 0.33, rms = 0.38), 5-fold cross-validation (MAE = 0.38, rms = 0.47), prediction of +/- values for an external test set (27/28 correct), and estimation of logBB values for a large data set of 20 039 drugs and drug-like compounds. Because no 3D structure information is used, computation of logBB by the model is very fast. The quality of the validation statistics supports the claim that the model may be used for estimation of logBB values for drug and drug-like molecules. Detailed structure interpretation is given for the structure indices in the model. The model indicates that molecules that penetrate the blood-brain barrier have large HS(T)(arom) values (presence of aromatic groups) but small values of HS(T)(HBd) (fewer or weaker H-Bond donors) and smaller d(2)chi(v) values (less branched molecules with fewer electronegative atoms). These three structure descriptors encode influence of molecular context of groups as well as counts of those groups.

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“…Electrotopological state descriptors have been shown to be highly correlated with Mulliken population and encode information about an atom's electron accessibility for external interactions (). These descriptors have been useful in other QSARs to help explain the importance of electronic properties of molecules for various biological and physicochemical effects ( − ). In this study, these descriptors were used to develop effective models to explain and predict insect toxicity of monoterpenoids and their derivatives.…”

Section: Introductionmentioning

confidence: 99%

“…Mulliken populations and an electrotopological state descriptor were used to represent electron density around certain atoms in the molecule. Electrotopological state descriptors have been shown to be highly correlated with Mulliken population and encode information about an atom's electron accessibility for external interactions (5) [6][7][8][9]. In this study, these descriptors were used to develop effective models to explain and predict insect toxicity of monoterpenoids and their derivatives.…”

Section: Introductionmentioning

confidence: 99%

QSAR Evaluation of Monoterpenoids' Insecticidal Activity

Grodnitzky

Coats

2002

J. Agric. Food Chem.

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Monoterpenoids are naturally occurring compounds that are found in higher-order plants. These compounds are secondary metabolites that seem to play no major role in the metabolic functioning of the plants. One role of monoterpenoids in the plants is to defend against plant-directed pathogens, herbivores, or competing plant species. These compounds are good leads for synthesis or isolation of more effective insecticides. To accomplish these goals, we developed quantitative structure−activity relationships (QSARs) in order to predict insect toxicity of monoterpenoids and derivatives that have not yet been synthesized or experimentally tested. Correlations were found between toxicity and certain quantum and traditional chemical parameters. We found a linear relationship between LD50 values for house fly toxicity and Mulliken populations in aromatic monoterpenoids. Multiple linear regression of an E-State descriptor and a GETAWAY (GEometry, Topology and Atomic Weights AssemblY) descriptor also showed a relationship with house fly toxicity for a wide range of monoterpenoids. KeywordsQuantitative structure−activity relationship (QSAR), monoterpenoid, insecticide, Mulliken population, quantum descriptor, electrotopological state Disciplines Entomology CommentsReprinted with permission from Journal of Agricultural and Food Chemistry 50 (2002) Monoterpenoids are naturally occurring compounds that are found in higher-order plants. These compounds are secondary metabolites that seem to play no major role in the metabolic functioning of the plants. One role of monoterpenoids in the plants is to defend against plant-directed pathogens, herbivores, or competing plant species. These compounds are good leads for synthesis or isolation of more effective insecticides. To accomplish these goals, we developed quantitative structureactivity relationships (QSARs) in order to predict insect toxicity of monoterpenoids and derivatives that have not yet been synthesized or experimentally tested. Correlations were found between toxicity and certain quantum and traditional chemical parameters. We found a linear relationship between LD 50 values for house fly toxicity and Mulliken populations in aromatic monoterpenoids. Multiple linear regression of an E-State descriptor and a GETAWAY (GEometry, Topology and Atomic Weights AssemblY) descriptor also showed a relationship with house fly toxicity for a wide range of monoterpenoids.

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Modeling the toxicity of amide herbicides using the electrotopological state

Cited by 37 publications

References 20 publications

Three new consensus QSAR models for the prediction of Ames genotoxicity

Three new consensus QSAR models for the prediction of Ames genotoxicity

Modeling Blood-Brain Barrier Partitioning Using the Electrotopological State

QSAR Evaluation of Monoterpenoids' Insecticidal Activity

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