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Computational Toxicology

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Cover of 'Computational Toxicology'

Table of Contents

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    Book Overview
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    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 12: Approaching Pharmacological Space: Events and Components
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Chapter title
Approaching Pharmacological Space: Events and Components
Chapter number 12
Book title
Computational Toxicology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7899-1_12
Pubmed ID
Book ISBNs
978-1-4939-7898-4, 978-1-4939-7899-1
Authors

Giulio Vistoli, Alessandro Pedretti, Angelica Mazzolari, Bernard Testa

Abstract

With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, physicochemical properties and binding spaces, which are seen as the three key elements composing the pharmacological space. While the concept of conformational space is routinely used to encode molecular flexibility, the concepts of property spaces and, particularly, of binding spaces are more innovative. Indeed, their descriptors can find fruitful applications (a) in describing the dynamic adaptability a given ligand experiences when inserted into a specific environment, and (b) in parameterizing the flexibility a ligand retains when bound to a biological target. Overall, these descriptors can conveniently account for the often disregarded entropic factors and as such they prove successful when inserted in ligand- or structure-based predictive models. Notably, and although binding space parameters can clearly be derived from MD simulations, the chapter will illustrate how docking calculations, despite their static nature, are able to evaluate ligand's flexibility by analyzing several poses for each ligand. Such an approach, which represents the founding core of the binding space concept, can find various applications in which the related descriptors show an impressive enhancing effect on the statistical performances of the resulting predictive models.

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 20%
Student > Bachelor 1 20%
Researcher 1 20%
Student > Postgraduate 1 20%
Student > Doctoral Student 1 20%
Other 0 0%
Readers by discipline Count As %
Chemistry 2 40%
Psychology 1 20%
Economics, Econometrics and Finance 1 20%
Agricultural and Biological Sciences 1 20%