<i>In Silico</i> Quantitative Structure–Toxicity Relationship Study of Aromatic Nitro Compounds

Pasha, Farhan Ahmad; Neaz, M. M.; Cho, Seung Joo; Ansari, Mohiuddin; Mishra, Sunil Kumar; Tiwari, Sharvan

doi:10.1111/j.1747-0285.2009.00799.x

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…In addition, several topological properties indicating the threedimensional (3D) structure were calculated using RDKit and CDK nodes in KNIME. The use of topological descriptors has been previously reported in a structure-toxicity relationship study (Pasha et al, 2009). Furthermore, topological descriptors have several advantages compared to 3D descriptors, including conformational independency, simplicity and low computational resources.…”

Section: Calculation and Combination Of Fingerprintsmentioning

confidence: 99%

Molecular similarity-based predictions of the Tox21 screening outcome

Drwal

Siramshetty

Banerjee

et al. 2015

Front. Environ. Sci.

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Citation:Drwal MN, Siramshetty VB, Banerjee P, Goede A, Preissner R and Dunkel M (2015) To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modeling general and complex endpoints to the investigation and modeling of pathways of toxicity and the underlying molecular effects. The U.S. Toxicology in the twenty-first century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models. Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modeled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.

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Section: Calculation and Combination Of Fingerprintsmentioning

confidence: 99%

Molecular similarity-based predictions of the Tox21 screening outcome

Drwal

Siramshetty

Banerjee

et al. 2015

Front. Environ. Sci.

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“…The study on the in silico quantitative structure toxicity relationship has been conducted by Pasha et al (2009) on aromatic nitro compounds. A total of 148 MINI REVIEW Figure 1.…”

Section: Sar and Qsarmentioning

confidence: 99%

In-silico approach in risk assessment of nutraceutical properties

Samsudin¹,

Hashim²,

Salleh³

2020

Food Res.

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Dietary supplements, including those containing botanical ingredients and botanicalderived compounds, have been marketed to consumers globally for many decades. However, the legislative framework for such products remains inconsistent across jurisdictions internationally. A common problem, concerning these nutraceutical products, is deficient information and lack of data for assessing the hazards posed to human health. The main objective is to explore the use of in silico tools in a risk assessment context of nutraceutical product, to relate properties of the molecular structure to the toxic effect of the chemical substance, by using principles and methods of computational chemistry. Further consideration of the actual impact of adverse events arising from nutraceutical food supplement usage will be helpful in guiding such issue as a potential for misidentification, and adulteration of botanical supplements by pharmacologically active substances.

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“…Cho et al, [23] discussed the in silico quantitative structure toxicity relationship of aromatic nitro compounds. Small compounds often have toxicities that are a function of molecular structural features.…”

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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In Silico Quantitative Structure–Toxicity Relationship Study of Aromatic Nitro Compounds

Cited by 8 publications

References 38 publications

Molecular similarity-based predictions of the Tox21 screening outcome

Molecular similarity-based predictions of the Tox21 screening outcome

In-silico approach in risk assessment of nutraceutical properties

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

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