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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
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    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.
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

3 tweeters
2 Google+ users


9 Dimensions

Readers on

103 Mendeley
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In Silico Methods for Predicting Drug Toxicity
Published by
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3609-0
978-1-4939-3607-6, 978-1-4939-3609-0

Emilio Benfenati

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Bulgaria 1 <1%
Sweden 1 <1%
Unknown 99 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 27 26%
Student > Ph. D. Student 19 18%
Researcher 15 15%
Student > Bachelor 10 10%
Other 8 8%
Other 12 12%
Unknown 12 12%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 31 30%
Biochemistry, Genetics and Molecular Biology 11 11%
Agricultural and Biological Sciences 10 10%
Chemistry 10 10%
Computer Science 7 7%
Other 18 17%
Unknown 16 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 10 September 2020.
All research outputs
of 16,368,434 outputs
Outputs from Methods in molecular biology
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Outputs of similar age
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Outputs of similar age from Methods in molecular biology
of 5 outputs
Altmetric has tracked 16,368,434 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 9,494 research outputs from this source. They receive a mean Attention Score of 2.6. This one has done well, scoring higher than 85% 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 265,448 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them