Computational Toxicology (eBook, PDF)

Risk Assessment for Chemicals
Redaktion: Ekins, Sean

• Format: PDF

Produktbeschreibung

A key resource for toxicologists across a broad spectrum of fields, this book offers a comprehensive analysis of molecular modelling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals. * Provides a perspective of what is currently achievable with computational toxicology and a view to future developments * Helps readers overcome questions of data sources, curation, treatment, and how to model / interpret critical endpoints that support 21st century hazard assessment * Assembles cutting-edge concepts and leading authors into a unique and powerful single-source reference * Includes in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modeling * Features coverage about consumer product safety assessment and chemical defense along with chapters on open source toxicology and big data

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 432
• Erscheinungstermin: 8. Januar 2018
• Englisch
• ISBN-13: 9781119282570
• Artikelnr.: 50877695

Autorenporträt

Sean Ekins, MSc, PhD, DSc has over 20 years of pharmaceutical and toxicology experience. He is the founder or co-founder of two companies and Adjunct Professor at three universities. He has been awarded 16 NIH grants as Principal Investigator. He has authored or co authored over 285 peer-reviewed papers and book chapters and edited five books with Wiley. His research is focused on collaborations to facilitate rare and neglected disease drug discovery.

Inhaltsangabe

List of Contributors xvii

Preface xxi

Acknowledgments xxiii

Part I Computational Methods 1

1 AccessibleMachine Learning Approaches for Toxicology 3
Sean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, Alexandru Korotcov, and Valery Tkachenko

1.1 Introduction 3

1.2 Bayesian Models 5

1.2.1 CDD Models 7

1.3 Deep LearningModels 13

1.4 Comparison of Different Machine LearningMethods 16

1.4.1 Classic Machine LearningMethods 17

1.4.1.1 Bernoulli Naive Bayes 17

1.4.1.2 Linear Logistic Regression with Regularization 18

1.4.1.3 AdaBoost Decision Tree 18

1.4.1.4 Random Forest 18

1.4.1.5 Support Vector Machine 19

1.4.2 Deep Neural Networks 19

1.4.3 Comparing Models 20

1.5 FutureWork 21

Acknowledgments 21

References 21

2 Quantum Mechanics Approaches in Computational Toxicology 31
Jakub Kostal

2.1 Translating Computational Chemistry to Predictive Toxicology 31

2.2 Levels of Theory in Quantum Mechanical Calculations 33

2.3 Representing Molecular Orbitals 38

2.4 Hybrid Quantum and Molecular Mechanical Calculations 39

2.5 Representing System Dynamics 40

2.6 Developing QM Descriptors 42

2.6.1 Global Electronic Parameters 42

2.6.1.1 Electrostatic Potential, Dipole, and Polarizability 43

2.6.1.2 Global Electronic Parameters Derived from Frontier Molecular Orbitals (FMOs) 45

2.6.2 Local (Atom-Based) Electronic Parameters 47

2.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) 48

2.6.2.2 Partial Atomic Charges 51

2.6.2.3 Hydrogen-Bonding Interactions 51

2.6.2.4 Bond Enthalpies 53

2.6.3 Modeling Chemical Reactions 53

2.6.4 QM/MM Calculations of Covalent Host-Guest Interactions 56

2.6.5 Medium Effects and Hydration Models 59

2.7 Rational Design of Safer Chemicals 61

References 64

Part II Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical 69

3 Computational Approaches for Predicting hERG Activity 71
Vinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade

3.1 Introduction 71

3.2 Computational Approaches 73

3.3 Ligand-Based Approaches 73

3.4 Structure-Based Approaches 77

3.5 Applications to Predict hERG Blockage 77

3.5.1 Pred-hERGWeb App 79

3.6 Other Computational Approaches Related to hERG Liability 82

3.7 Final Remarks 83

References 83

4 Computational Toxicology for Traditional Chinese Medicine 93
Ni Ai and Xiaohui Fan

4.1 Background, Current Status, and Challenges 93

4.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 99

4.2.1 Introduction to OAT1 and TCM 99

4.2.2 Construction of TCM Compound Database 101

4.2.3 OAT1 Inhibitor Pharmacophore Development 101

4.2.4 External Test Set Evaluation 102

4.2.5 Database Searching 102

4.2.6 Results: OAT1 Inhibitor Pharmacophore 103

4.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation 104

4.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore 104

4.2.9 Discussion 110

4.3 Conclusion 114

Acknowledgment 114

References 114

5 PharmacophoreModels for Toxicology Prediction 121
Daniela Schuster

5.1 Introduction 121

5.2 Antitarget Screening 125