Multiple computer‐automated structure evaluation study of aquatic toxicity. III. <i>Vibrio fischeri</i>

Klopman, Gilles; Stuart, Scott E.

doi:10.1002/etc.5620220302

Cited by 14 publications

(1 citation statement)

References 24 publications

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“…The ER data set was collected from previous estrogen receptor binding studies and specifically refers to the chemical binding affinity of ERα . The EB data set contains the results of Microtox testing of environmental bacteria (aerobic heterotrophs, nitrosomonas, methanogens, and photobacteria) by U.S. EPA. , The remaining two data sets, FM and LD50, are whole animal toxicity endpoints, and represent the acute toxicity testing results against the fathead minnow and rat, respectively. , …”

Section: Methodsmentioning

confidence: 99%

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

et al. 2017

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Numerous chemical data sets have become available for quantitative structure–activity relationship (QSAR) modeling studies. However, the quality of different data sources may be different based on the nature of experimental protocols. Therefore, potential experimental errors in the modeling sets may lead to the development of poor QSAR models and further affect the predictions of new compounds. In this study, we explored the relationship between the ratio of questionable data in the modeling sets, which was obtained by simulating experimental errors, and the QSAR modeling performance. To this end, we used eight data sets (four continuous endpoints and four categorical endpoints) that have been extensively curated both in-house and by our collaborators to create over 1800 various QSAR models. Each data set was duplicated to create several new modeling sets with different ratios of simulated experimental errors (i.e., randomizing the activities of part of the compounds) in the modeling process. A fivefold cross-validation process was used to evaluate the modeling performance, which deteriorates when the ratio of experimental errors increases. All of the resulting models were also used to predict external sets of new compounds, which were excluded at the beginning of the modeling process. The modeling results showed that the compounds with relatively large prediction errors in cross-validation processes are likely to be those with simulated experimental errors. However, after removing a certain number of compounds with large prediction errors in the cross-validation process, the external predictions of new compounds did not show improvement. Our conclusion is that the QSAR predictions, especially consensus predictions, can identify compounds with potential experimental errors. But removing those compounds by the cross-validation procedure is not a reasonable means to improve model predictivity due to overfitting.

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Section: Methodsmentioning

confidence: 99%

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

et al. 2017

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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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MultiCASE Platform for In Silico Toxicology

Chakravarti

Saiakhov

2022

Methods in Molecular Biology

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Multiple computer‐automated structure evaluation study of aquatic toxicity. III. Vibrio fischeri

Cited by 14 publications

References 24 publications

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do

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

MultiCASE Platform for In Silico Toxicology

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