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Computational Toxicology

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Cover of 'Computational Toxicology'

Table of Contents

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    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 1: Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach
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Chapter title
Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach
Chapter number 1
Book title
Computational Toxicology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7899-1_1
Pubmed ID
Book ISBNs
978-1-4939-7898-4, 978-1-4939-7899-1
Authors

Francesca Grisoni, Davide Ballabio, Roberto Todeschini, Viviana Consonni, Grisoni, Francesca, Ballabio, Davide, Todeschini, Roberto, Consonni, Viviana

Abstract

Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure-activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 88 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 17%
Student > Master 12 14%
Student > Bachelor 11 13%
Researcher 7 8%
Student > Doctoral Student 6 7%
Other 11 13%
Unknown 26 30%
Readers by discipline Count As %
Chemistry 26 30%
Pharmacology, Toxicology and Pharmaceutical Science 9 10%
Biochemistry, Genetics and Molecular Biology 8 9%
Chemical Engineering 4 5%
Computer Science 3 3%
Other 8 9%
Unknown 30 34%