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In Silico Methods for Predicting Drug Toxicity

Overview of attention for book
Cover of 'In Silico Methods for Predicting Drug Toxicity'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 QSAR Methods.
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    Chapter 2 In Silico 3D Modeling of Binding Activities.
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    Chapter 3 Modeling Pharmacokinetics.
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    Chapter 4 Modeling ADMET.
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    Chapter 5 In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results.
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    Chapter 6 In Silico Methods for Carcinogenicity Assessment.
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    Chapter 7 VirtualToxLab: Exploring the Toxic Potential of Rejuvenating Substances Found in Traditional Medicines.
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    Chapter 8 In Silico Model for Developmental Toxicity: How to Use QSAR Models and Interpret Their Results.
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    Chapter 9 In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Level (NOAEL) and Lowest Observed Adverse Effect Level (LOAEL) for Drugs.
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    Chapter 10 In Silico Models for Acute Systemic Toxicity.
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    Chapter 11 In Silico Models for Hepatotoxicity.
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    Chapter 12 In Silico Models for Ecotoxicity of Pharmaceuticals.
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    Chapter 13 Use of Read-Across Tools.
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    Chapter 14 Adverse Outcome Pathways as Tools to Assess Drug-Induced Toxicity.
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    Chapter 15 A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure.
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    Chapter 16 In Silico Study of In Vitro GPCR Assays by QSAR Modeling.
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    Chapter 17 Taking Advantage of Databases.
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    Chapter 18 QSAR Models at the US FDA/NCTR.
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    Chapter 19 A Round Trip from Medicinal Chemistry to Predictive Toxicology.
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    Chapter 20 The Use of In Silico Models Within a Large Pharmaceutical Company.
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    Chapter 21 The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities.
Attention for Chapter 5: In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results.
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog

Citations

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30 Dimensions

Readers on

mendeley
14 Mendeley
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Chapter title
In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their Results.
Chapter number 5
Book title
In Silico Methods for Predicting Drug Toxicity
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3609-0_5
Pubmed ID
27311463
Book ISBNs
978-1-4939-3607-6, 978-1-4939-3609-0
Authors

Enrico Mombelli, Giuseppa Raitano, Emilio Benfenati, Mombelli, Enrico, Raitano, Giuseppa, Benfenati, Emilio

Editors

Emilio Benfenati

Abstract

Information on genotoxicity is an essential piece of information gathering for a comprehensive toxicological characterization of chemicals. Several QSAR models that can predict Ames genotoxicity are freely available for download from the Internet and they can provide relevant information for the toxicological profiling of chemicals. Indeed, they can be straightforwardly used for predicting the presence or absence of genotoxic hazards associated with the interactions of chemicals with DNA.Nevertheless, and despite the ease of use of these models, the scientific challenge is to assess the reliability of information that can be obtained from these tools. This chapter provides instructions on how to use freely available QSAR models and on how to interpret their predictions.

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Bulgaria 1 7%
Unknown 13 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Other 3 21%
Student > Bachelor 2 14%
Student > Ph. D. Student 1 7%
Professor 1 7%
Other 0 0%
Unknown 3 21%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 5 36%
Biochemistry, Genetics and Molecular Biology 1 7%
Computer Science 1 7%
Social Sciences 1 7%
Medicine and Dentistry 1 7%
Other 0 0%
Unknown 5 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 September 2021.
All research outputs
#5,762,276
of 22,879,161 outputs
Outputs from Methods in molecular biology
#1,628
of 13,132 outputs
Outputs of similar age
#91,604
of 393,703 outputs
Outputs of similar age from Methods in molecular biology
#207
of 1,471 outputs
Altmetric has tracked 22,879,161 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 13,132 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,703 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 1,471 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.

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