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Computational Toxicology

Overview of attention for book
Cover of 'Computational Toxicology'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach
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    Chapter 2 The OECD QSAR Toolbox Starts Its Second Decade
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    Chapter 3 QSAR: What Else?
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    Chapter 4 (Q)SARs as Adaptations to REACH Information Requirements
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    Chapter 5 Machine Learning Methods in Computational Toxicology
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    Chapter 6 Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling
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    Chapter 7 Molecular Similarity in Computational Toxicology
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    Chapter 8 Molecular Docking for Predictive Toxicology
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    Chapter 9 Criteria and Application on the Use of Nontesting Methods within a Weight of Evidence Strategy
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    Chapter 10 Characterization and Management of Uncertainties in Toxicological Risk Assessment: Examples from the Opinions of the European Food Safety Authority
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    Chapter 11 Computational Toxicology and Drug Discovery
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    Chapter 12 Approaching Pharmacological Space: Events and Components
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    Chapter 13 Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation
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    Chapter 14 Enalos Suite: New Cheminformatics Platform for Drug Discovery and Computational Toxicology
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    Chapter 15 Ion Channels in Drug Discovery and Safety Pharmacology
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    Chapter 16 Computational Approaches in Multitarget Drug Discovery
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    Chapter 17 Nanoformulations for Drug Delivery: Safety, Toxicity, and Efficacy
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    Chapter 18 Toxicity Potential of Nutraceuticals
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    Chapter 19 Impact of Pharmaceuticals on the Environment: Risk Assessment Using QSAR Modeling Approach
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    Chapter 20 (Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks
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    Chapter 21 Stem Cell-Based Methods to Predict Developmental Chemical Toxicity
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    Chapter 22 Predicting Chemically Induced Skin Sensitization by Using In Chemico / In Vitro Methods
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    Chapter 23 Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways Using Gene Expression Data
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    Chapter 24 Nontest Methods to Predict Acute Toxicity: State of the Art for Applications of In Silico Methods
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    Chapter 25 Predictive Systems Toxicology
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    Chapter 26 Chemoinformatic Approach to Assess Toxicity of Ionic Liquids
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    Chapter 27 Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results
Attention for Chapter 8: Molecular Docking for Predictive Toxicology
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Chapter title
Molecular Docking for Predictive Toxicology
Chapter number 8
Book title
Computational Toxicology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7899-1_8
Pubmed ID
Book ISBNs
978-1-4939-7898-4, 978-1-4939-7899-1
Authors

Daniela Trisciuzzi, Domenico Alberga, Francesco Leonetti, Ettore Novellino, Orazio Nicolotti, Giuseppe F. Mangiatordi, Trisciuzzi, Daniela, Alberga, Domenico, Leonetti, Francesco, Novellino, Ettore, Nicolotti, Orazio, Mangiatordi, Giuseppe F.

Abstract

Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Doctoral Student 2 13%
Student > Bachelor 2 13%
Student > Postgraduate 2 13%
Student > Ph. D. Student 1 6%
Unspecified 1 6%
Other 2 13%
Unknown 6 38%
Readers by discipline Count As %
Chemistry 2 13%
Unspecified 1 6%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Agricultural and Biological Sciences 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 2 13%
Unknown 8 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 June 2018.
All research outputs
#15,538,060
of 23,092,602 outputs
Outputs from Methods in molecular biology
#5,410
of 13,207 outputs
Outputs of similar age
#270,127
of 442,643 outputs
Outputs of similar age from Methods in molecular biology
#596
of 1,499 outputs
Altmetric has tracked 23,092,602 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,207 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 442,643 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.