QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

Roy, Kunal; Das, Rudra Narayan

doi:10.1016/j.jhazmat.2010.07.116

Cited by 25 publications

(9 citation statements)

References 68 publications

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“…For those benzoic acid compounds where complex QSTR computations were available, it was shown that Eqs. (2) and (3) provide more reliable predictions. It was also indicated that the new method can give surprisingly good results of toxicity in mice via oral for further 324 complex benzoic acid compounds without using any computer code.…”

Section: Discussionmentioning

confidence: 99%

“…(1) at the design stage of new compounds and compound libraries, (2) optimization of the screening and testing by looking at only the most promising compounds.…”

Section: Introductionmentioning

confidence: 99%

“…The success of any QSAR and QSTR model depends on several factors such as accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. However, they have some limitations in their applications: (1) they require special computer codes; (2) they are limited to the congeneric series; (3) large numbers of compounds are required to derive a good model; (4) they depend largely on the use of reliable data; and (5) QSAR or QSTR for "predictive" purposes is not a realistic expectation [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes

Keshavarz¹,

Gharagheizi²,

Shokrolahi³

et al. 2012

Journal of Hazardous Materials

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Section: Discussionmentioning

confidence: 99%

“…(1) at the design stage of new compounds and compound libraries, (2) optimization of the screening and testing by looking at only the most promising compounds.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes

Keshavarz¹,

Gharagheizi²,

Shokrolahi³

et al. 2012

Journal of Hazardous Materials

View full text Add to dashboard Cite

“…The second dataset used belonged to a recent QSAR study of C allosteric binding site of 115 Hepatitis C Virus (HCV) NS5B polymerase inhibitors21 for which both the experimental data and the numerical values of the selected chemical descriptors were published. The third dataset belonged to a recent QSAR study of 40‐hour toxicity of 77 aromatic aldehydes to Tetrahymena pyriformis 22…”

Section: Methodsmentioning

confidence: 99%

Modelling Structure Activity Landscapes with Cliffs: a Kernel Regression‐Based Approach

Tebby

Mombelli

2013

Molecular Informatics

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Quantitative Structure-Activity Relationship (QSAR) models are increasingly used in hazard and risk assessment. Even when models with linear relationships between activity and a small number of descriptors are built and validated regarding predictivity and statistical assumptions, similar structures can exhibit large differences in activity known as similarity paradoxes or activity cliffs. In order to reduce the impact that similarity paradoxes can have on predictions we have devised a statistical method based on Nadaraya-Watson kernel regression. According to our method, activity cliffs filter out contributions of neighbouring chemicals especially along the cliff axis. Our method decreases density-based certainty in particular for chemicals with strong prediction errors and the implementation of Structure-Activity Landscape Index (SALI) curves shows that our method improves the prediction of activity cliff ranks. We also provide useful indications on the density-based applicability domain and the reliability of individual predictions.

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“…All the models have been validated using internal and external validation techniques [33]. The ETA models have been compared with those developed using various non-ETA topological indices.…”

Section: Quantitative Structure Activity Relat-ionship (Qsar) In Toximentioning

confidence: 99%

In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies

Deeb

Goodarzi

2012

Current Drug Safety

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Undesirable toxicity is still a major block in the drug discovery process. Obviously, capable techniques that identify poor effects at a very early stage of product development and provide reasonable toxicity estimates for the huge number of untested compounds are needed. In silico techniques are very useful for this purpose, because of their advantage in reducing time and cost.These case studies give the description of in silico validation techniques and applied modeling methods for the prediction of toxicity of chemical compounds. In silico toxicity prediction techniques can be classified into two categories: Molecular Modeling and methods that derive predictions from experimental data.Molecular modeling is a computational approach to mimic the behavior of molecules, from small molecules (e.g. in conformational analysis) to biomolecules. But the same approaches can also be applied for toxicological purposes, if the mechanism is receptor mediated.Quantitative Structure-Toxicity Relationships (QSTRs) models are typical examples for the prediction of toxicity which relates variations in the molecular structures to toxicity.

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QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

Cited by 25 publications

References 68 publications

Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes

Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes

Modelling Structure Activity Landscapes with Cliffs: a Kernel Regression‐Based Approach

In Silico Quantitative Structure Toxicity Relationship of Chemical Compounds: Some Case Studies

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