Multivariate Data Analysis in Structure—Activity Relationships

Lewi, Paul

doi:10.1016/b978-0-12-060310-7.50013-4

Cited by 18 publications

(6 citation statements)

References 32 publications

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“…Different ETA descriptors calculated for the nitrobenzene derivatives are defined in Table 3. Factor analysis has been performed as the data preprocessing step for identification of important descriptors for the subsequent multiple regression analysis [42,43]. For this purpose, the data matrix consisting of the descriptors has been subjected to principal component factor analysis using STATISTICA software [44].…”

Section: Methodsmentioning

confidence: 99%

QSTR with Extended Topochemical Atom Indices. 3. Toxicity of Nitrobenzenes to Tetrahymena pyriformis

Roy

Ghosh

2004

QSAR Comb. Sci.

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The experimental determination of toxicological properties of commercial chemicals being costly and time consuming process, there is a need to develop mathematical predictive tool to theoretically quantify such properties. In this background, we have modeled toxicity of nitrobenzene derivatives to Tetrahymena pyriformis using extended topochemical atom (ETA) indices recently introduced by us (Roy and Ghosh, 2003). We have also modeled the toxicity data using other topological descriptors (Balaban J, kappa shape indices, connectivity indices, Wiener index) and two physicochemical variables (AlogP98, MolRef) and compared the ETA models with non-ETA ones. Principal component factor analysis was used as the data-preprocessing step to reduce the dimensionality of the data matrix and identify the important variables that are devoid of collinearities. Multiple linear regression analyses show that the best non-ETA model involves (size), halogen and additional nitro substitutions in the nitrobenzene ring and negative contributions of the substituents like methyl and hydroxymethyl groups to the toxicity. An attempt to use non-ETA descriptors along with the ETA ones slightly improves the quality in comparison to the best ETA model. Interestingly, the ETA model developed by us for the nitrobenzene toxicity is comparable to the previously reported models on the same data set (Estrada et al., 2001;Cronin et al., 1998). Thus, it appears that the ETA descriptors have significant potential in QSAR/QSPR/ QSTR studies, which warrants extensive evaluation.

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Section: Methodsmentioning

confidence: 99%

QSTR with Extended Topochemical Atom Indices. 3. Toxicity of Nitrobenzenes to Tetrahymena pyriformis

Roy

Ghosh

2004

QSAR Comb. Sci.

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“…Attempt was made to perform PCRA [40] taking factor scores of the descriptor matrix (without toxicity value) as the predictor variables and adopting backward stepwise regression method. In this case, the principal components serve as latent variables.…”

Section: Statistical Analysis Performedmentioning

confidence: 99%

“…In the case of FA-MLR, classical approach of multiple regression technique used as the final statistical tool for developing QSAR relations and FA [40,41] was used as the data preprocessing step to identify the important predictor variables contributing to the response variable (i.e. toxicity value) and to avoid collinearities among them.…”

Section: Statistical Analysis Performedmentioning

confidence: 99%

QSTR with extended topochemical atom (ETA) indices. 11. Comparative QSAR of acute NSAID cytotoxicity in rat hepatocytes using chemometric tools

Roy

Ghosh

2009

Molecular Simulation

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An attempt was made to develop quantitative structure -toxicity relationships (QSTRs) for acute rat hepatocyte cytotoxicity of a series of non-steroidal anti-inflammatory drugs (NSAIDs) with extended topochemical atom (ETA) indices. The ETA models were compared with those developed with a pool of other topological indices. Finally, attempt was made to develop models from the combined pool of topological (ETA and non-ETA) descriptors. The chemometric tools used for model development were multiple linear regression with factor analysis as the variable selection tool, stepwise regression, principal component regression analysis, partial least squares (PLS), genetic function approximation (GFA) and genetic PLS (G/PLS). Use of ETA descriptors along with non-ETA ones increased the statistical quality of the non-ETA models in different techniques except stepwise regression and GFA. The best three models came from GFA, G/PLS and PLS techniques (leave-one-out Q 2 values of 0.871, 0.854 and 0.834, respectively, using combined set of descriptors except for GFA). Use of the ETA parameters suggests that the toxicity increases with bulk and degree of branching. Moreover, heteroatom count and degree of unsaturation are also important for the toxicity.

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“…The statistical analyses were carried out using MLR and PLS as the statistical tools. Different methods of variable selection like stepwise regression, genetic method and factor analysis (32,33) as the data preprocessing step for the identification of important descriptors for the final multiple regression analysis were used. In case of PLS, variables were gradually removed form the whole pool of descriptors in a stepwise manner based on standardized regression coefficients.…”

Section: Statistical Analysis Performedmentioning

confidence: 99%

“…In case of FA-MLR, classical approach of multiple regression technique was used as the final statistical tool for developing QSAR relations and factor analysis (FA) (32,33) was used as the data-preprocessing step to identify the important predictor variables contributing to the response variable and to avoid collinearities among them. In a typical factor analysis procedure, the data matrix is first standardized, and correlation matrix and subsequently the reduced correlation matrix are constructed.…”

Section: Statistical Analysis Performedmentioning

confidence: 99%

QSTR with Extended Topochemical Atom Indices. 10. Modeling of Toxicity of Organic Chemicals to Humans Using Different Chemometric Tools

Roy¹,

Ghosh²

2008

Chem Biol Drug Des

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In this communication, we have developed quantitative predictive models using human lethal concentration values of 26 organic compounds including some pharmaceuticals with extended topochemical atom (ETA) indices applying different chemometric tools and compared the extended topochemical atom models with the models developed from non-extended topochemical atom ones. Extended topochemical atom descriptors were also tried in combination with non-extended topochemical atom descriptors to develop better predictive models. The use of extended topochemical atom descriptors along with non-extended topochemical atom ones improved equation statistics and cross-validation quality. The best model with sound statistical quality was developed from partial least squares regression using extended topochemical atom descriptors in combination non-extended topochemical atom ones. Finally, to check true predictability of the ETA parameters, the data set was divided into training (n = 19) and test (n = 7) sets. Partial least squares and genetic partial least squares models were developed from the training set using extended topochemical atom indices and the models were validated using the test set. The extended topochemical atom models developed from different statistical tools suggest that the toxicity increases with bulk, chloro functionality, presence of electronegative atoms within a chain or ring and unsaturation, and decreases with hydroxy functionality and branching. The results suggest that the extended topochemical atom descriptors are sufficiently rich in chemical information to encode the structural features for QSAR/QSPR/QSTR modeling.

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Multivariate Data Analysis in Structure—Activity Relationships

Cited by 18 publications

References 32 publications

QSTR with Extended Topochemical Atom Indices. 3. Toxicity of Nitrobenzenes to Tetrahymena pyriformis

QSTR with Extended Topochemical Atom Indices. 3. Toxicity of Nitrobenzenes to Tetrahymena pyriformis

QSTR with extended topochemical atom (ETA) indices. 11. Comparative QSAR of acute NSAID cytotoxicity in rat hepatocytes using chemometric tools

QSTR with Extended Topochemical Atom Indices. 10. Modeling of Toxicity of Organic Chemicals to Humans Using Different Chemometric Tools

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