{"product_id":"computational-toxicology-9781119282563","title":"Computational Toxicology","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eA 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides a perspective of what is currently achievable with computational toxicology and a view to future developments\u003c\/li\u003e \u003cli\u003eHelps readers overcome questions of data sources, curation, treatment, and how to model \/ interpret critical endpoints that support 21st century hazard assessment\u003c\/li\u003e \u003cli\u003eAssembles cutting-edge concepts and leading authors into a unique and powerful single-source reference\u003c\/li\u003e \u003cli\u003eIncludes in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modeling\u003c\/li\u003e \u003cli\u003eFeatures coverage about consumer product safety assessment and chemical defense along with chapters on open source toxicology and big data\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Contributors xvii\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAcknowledgments xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Computational Methods 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 AccessibleMachine Learning Approaches for Toxicology 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSean Ekins, Alex M. Clark, Alexander L. Perryman, Joel S. Freundlich, Alexandru Korotcov, and Valery\u003c\/i\u003e \u003ci\u003eTkachenko\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Bayesian Models 5\u003c\/p\u003e \u003cp\u003e1.2.1 CDD Models 7\u003c\/p\u003e \u003cp\u003e1.3 Deep LearningModels 13\u003c\/p\u003e \u003cp\u003e1.4 Comparison of Different Machine LearningMethods 16\u003c\/p\u003e \u003cp\u003e1.4.1 Classic Machine LearningMethods 17\u003c\/p\u003e \u003cp\u003e1.4.1.1 Bernoulli Naive Bayes 17\u003c\/p\u003e \u003cp\u003e1.4.1.2 Linear Logistic Regression with Regularization 18\u003c\/p\u003e \u003cp\u003e1.4.1.3 AdaBoost Decision Tree 18\u003c\/p\u003e \u003cp\u003e1.4.1.4 Random Forest 18\u003c\/p\u003e \u003cp\u003e1.4.1.5 Support Vector Machine 19\u003c\/p\u003e \u003cp\u003e1.4.2 Deep Neural Networks 19\u003c\/p\u003e \u003cp\u003e1.4.3 Comparing Models 20\u003c\/p\u003e \u003cp\u003e1.5 FutureWork 21\u003c\/p\u003e \u003cp\u003eAcknowledgments 21\u003c\/p\u003e \u003cp\u003eReferences 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Quantum Mechanics Approaches in Computational Toxicology 31\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJakub Kostal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Translating Computational Chemistry to Predictive Toxicology 31\u003c\/p\u003e \u003cp\u003e2.2 Levels of Theory in Quantum Mechanical Calculations 33\u003c\/p\u003e \u003cp\u003e2.3 Representing Molecular Orbitals 38\u003c\/p\u003e \u003cp\u003e2.4 Hybrid Quantum and Molecular Mechanical Calculations 39\u003c\/p\u003e \u003cp\u003e2.5 Representing System Dynamics 40\u003c\/p\u003e \u003cp\u003e2.6 Developing QM Descriptors 42\u003c\/p\u003e \u003cp\u003e2.6.1 Global Electronic Parameters 42\u003c\/p\u003e \u003cp\u003e2.6.1.1 Electrostatic Potential, Dipole, and Polarizability 43\u003c\/p\u003e \u003cp\u003e2.6.1.2 Global Electronic Parameters Derived from Frontier Molecular Orbitals (FMOs) 45\u003c\/p\u003e \u003cp\u003e2.6.2 Local (Atom-Based) Electronic Parameters 47\u003c\/p\u003e \u003cp\u003e2.6.2.1 Parameters Derived from Frontier Molecular Orbitals (FMOs) 48\u003c\/p\u003e \u003cp\u003e2.6.2.2 Partial Atomic Charges 51\u003c\/p\u003e \u003cp\u003e2.6.2.3 Hydrogen-Bonding Interactions 51\u003c\/p\u003e \u003cp\u003e2.6.2.4 Bond Enthalpies 53\u003c\/p\u003e \u003cp\u003e2.6.3 Modeling Chemical Reactions 53\u003c\/p\u003e \u003cp\u003e2.6.4 QM\/MM Calculations of Covalent Host-Guest Interactions 56\u003c\/p\u003e \u003cp\u003e2.6.5 Medium Effects and Hydration Models 59\u003c\/p\u003e \u003cp\u003e2.7 Rational Design of Safer Chemicals 61\u003c\/p\u003e \u003cp\u003eReferences 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Applying Computers to Toxicology Assessment: Pharmaceutical, Industrial and Clinical 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Computational Approaches for Predicting hERG Activity 71\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVinicius M. Alves, Rodolpho C. Braga, and Carolina Horta Andrade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 71\u003c\/p\u003e \u003cp\u003e3.2 Computational Approaches 73\u003c\/p\u003e \u003cp\u003e3.3 Ligand-Based Approaches 73\u003c\/p\u003e \u003cp\u003e3.4 Structure-Based Approaches 77\u003c\/p\u003e \u003cp\u003e3.5 Applications to Predict hERG Blockage 77\u003c\/p\u003e \u003cp\u003e3.5.1 Pred-hERGWeb App 79\u003c\/p\u003e \u003cp\u003e3.6 Other Computational Approaches Related to hERG Liability 82\u003c\/p\u003e \u003cp\u003e3.7 Final Remarks 83\u003c\/p\u003e \u003cp\u003eReferences 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Computational Toxicology for Traditional Chinese Medicine 93\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNi Ai and Xiaohui Fan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Background, Current Status, and Challenges 93\u003c\/p\u003e \u003cp\u003e4.2 Case Study: Large-Scale Prediction on Involvement of Organic Anion Transporter 1 in Traditional Chinese Medicine-Drug Interactions 99\u003c\/p\u003e \u003cp\u003e4.2.1 Introduction to OAT1 and TCM 99\u003c\/p\u003e \u003cp\u003e4.2.2 Construction of TCM Compound Database 101\u003c\/p\u003e \u003cp\u003e4.2.3 OAT1 Inhibitor Pharmacophore Development 101\u003c\/p\u003e \u003cp\u003e4.2.4 External Test Set Evaluation 102\u003c\/p\u003e \u003cp\u003e4.2.5 Database Searching 102\u003c\/p\u003e \u003cp\u003e4.2.6 Results: OAT1 Inhibitor Pharmacophore 103\u003c\/p\u003e \u003cp\u003e4.2.7 Results: OAT1 Inhibitor Pharmacophore Evaluation 104\u003c\/p\u003e \u003cp\u003e4.2.8 Results: TCM Compound Database Searching Using OAT1 Inhibitor Pharmacophore 104\u003c\/p\u003e \u003cp\u003e4.2.9 Discussion 110\u003c\/p\u003e \u003cp\u003e4.3 Conclusion 114\u003c\/p\u003e \u003cp\u003eAcknowledgment 114\u003c\/p\u003e \u003cp\u003eReferences 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 PharmacophoreModels for Toxicology Prediction 121\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDaniela Schuster\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 121\u003c\/p\u003e \u003cp\u003e5.2 Antitarget Screening 125\u003c\/p\u003e \u003cp\u003e5.3 Prediction of Liver Toxicity 125\u003c\/p\u003e \u003cp\u003e5.4 Prediction of Cardiovascular Toxicity 127\u003c\/p\u003e \u003cp\u003e5.5 Prediction of Central Nervous System (CNS) Toxicity 128\u003c\/p\u003e \u003cp\u003e5.6 Prediction of Endocrine Disruption 130\u003c\/p\u003e \u003cp\u003e5.7 Prediction of ADME 135\u003c\/p\u003e \u003cp\u003e5.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies 136\u003c\/p\u003e \u003cp\u003eReferences 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Transporters in Hepatotoxicity 145\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 145\u003c\/p\u003e \u003cp\u003e6.2 Basolateral Transporters 146\u003c\/p\u003e \u003cp\u003e6.3 Canalicular Transporters 148\u003c\/p\u003e \u003cp\u003e6.4 Data Sources for Transporters in Hepatotoxicity 148\u003c\/p\u003e \u003cp\u003e6.5 In Silico Transporters Models 150\u003c\/p\u003e \u003cp\u003e6.6 Ligand-Based Approaches 150\u003c\/p\u003e \u003cp\u003e6.7 OATP1B1 and OATP1B3 150\u003c\/p\u003e \u003cp\u003e6.8 NTCP 154\u003c\/p\u003e \u003cp\u003e6.9 OCT1 154\u003c\/p\u003e \u003cp\u003e6.10 OCT2 154\u003c\/p\u003e \u003cp\u003e6.11 MRP1, MRP3, and MRP4 155\u003c\/p\u003e \u003cp\u003e6.12 BSEP 155\u003c\/p\u003e \u003cp\u003e6.13 MRP2 156\u003c\/p\u003e \u003cp\u003e6.14 MDR1\/P-gp 156\u003c\/p\u003e \u003cp\u003e6.15 MDR3 157\u003c\/p\u003e \u003cp\u003e6.16 BCRP 157\u003c\/p\u003e \u003cp\u003e6.17 MATE1 158\u003c\/p\u003e \u003cp\u003e6.18 ASBT 159\u003c\/p\u003e \u003cp\u003e6.19 Structure-Based Approaches 159\u003c\/p\u003e \u003cp\u003e6.20 Complex Models Incorporating Transporter Information 160\u003c\/p\u003e \u003cp\u003e6.21 In Vitro Models 160\u003c\/p\u003e \u003cp\u003e6.22 Multiscale Models 161\u003c\/p\u003e \u003cp\u003e6.23 Outlook 162\u003c\/p\u003e \u003cp\u003eAcknowledgments 164\u003c\/p\u003e \u003cp\u003eReferences 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Cheminformatics in a Clinical Setting 175\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMatthew D. Krasowski and Sean Ekins\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 175\u003c\/p\u003e \u003cp\u003e7.2 Similarity Analysis Applied to Drug of Abuse\/Toxicology Immunoassays 177\u003c\/p\u003e \u003cp\u003e7.3 Similarity Analysis Applied toTherapeutic Drug Monitoring Immunoassays 187\u003c\/p\u003e \u003cp\u003e7.4 Similarity Analysis Applied to Steroid Hormone Immunoassays 191\u003c\/p\u003e \u003cp\u003e7.5 Cheminformatics Applied to \"Designer Drugs\" 195\u003c\/p\u003e \u003cp\u003e7.6 Relevance to Antibody-Ligand Interactions 202\u003c\/p\u003e \u003cp\u003e7.7 Conclusions and Future Directions 203\u003c\/p\u003e \u003cp\u003eAcknowledgment 204\u003c\/p\u003e \u003cp\u003eReferences 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Computational Tools for ADMET Profiling 213\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDenis Fourches, Antony J.Williams, Grace Patlewicz, Imran Shah, Chris Grulke, JohnWambaugh, Ann\u003c\/i\u003e \u003ci\u003eRichard, and Alexander Tropsha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 213\u003c\/p\u003e \u003cp\u003e8.2 Cheminformatics Approaches for ADMET Profiling 214\u003c\/p\u003e \u003cp\u003e8.2.1 Chemical Data Curation Prior to ADMET Modeling 215\u003c\/p\u003e \u003cp\u003e8.2.2 QSAR Modelability Index 217\u003c\/p\u003e \u003cp\u003e8.2.3 Predictive QSAR Model DevelopmentWorkflow 218\u003c\/p\u003e \u003cp\u003e8.2.4 Hybrid QSAR Modeling 220\u003c\/p\u003e \u003cp\u003e8.2.4.1 Simple Consensus 223\u003c\/p\u003e \u003cp\u003e8.2.4.2 Mixed Chemical and Biological Features 223\u003c\/p\u003e \u003cp\u003e8.2.4.3 Two-Step HierarchicalWorkflow 224\u003c\/p\u003e \u003cp\u003e8.2.5 Chemical Biological Read-Across 226\u003c\/p\u003e \u003cp\u003e8.2.6 Public Chemotype Approach to Data-Mining 229\u003c\/p\u003e \u003cp\u003e8.3 Unsolved Challenges in Structure Based Profiling 230\u003c\/p\u003e \u003cp\u003e8.3.1 Biological Data Curation 231\u003c\/p\u003e \u003cp\u003e8.3.2 Identification and Treatment of Activity and Toxicity Cliffs 233\u003c\/p\u003e \u003cp\u003e8.3.3 In Vitro to In Vivo Continuum in the Context of AOP 233\u003c\/p\u003e \u003cp\u003e8.4 Perspectives 234\u003c\/p\u003e \u003cp\u003e8.4.1 Profilers on the Go with Mobile Devices 235\u003c\/p\u003e \u003cp\u003e8.4.2 Structure–Exposure–Activity Relationships 236\u003c\/p\u003e \u003cp\u003e8.5 Conclusions 237\u003c\/p\u003e \u003cp\u003eAcknowledgments 237\u003c\/p\u003e \u003cp\u003eDisclaimer 237\u003c\/p\u003e \u003cp\u003eReferences 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Computational Toxicology and Reach 245\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEmilio Enfenati, Anna Lombardo, and Alessandra Roncaglioni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 245\u003c\/p\u003e \u003cp\u003e9.2 Reach and the Other Legislations 247\u003c\/p\u003e \u003cp\u003e9.3 Annex XI of Reach for QSARModels 248\u003c\/p\u003e \u003cp\u003e9.3.1 The First Condition of Annex XI and QMRF 249\u003c\/p\u003e \u003cp\u003e9.3.2 The Second Condition and the Applicability Domain 251\u003c\/p\u003e \u003cp\u003e9.3.3 TheThird Condition of Annex XI, and the Use of the QSAR Models 252\u003c\/p\u003e \u003cp\u003e9.3.4 Adequate and Reliable Documentation of the Applied Method 254\u003c\/p\u003e \u003cp\u003e9.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 255\u003c\/p\u003e \u003cp\u003e9.4.1 Example of Bioconcentration Factor (BCF) 255\u003c\/p\u003e \u003cp\u003e9.4.2 Example of Mutagenicity (Reverse-Mutation Assay) Prediction 260\u003c\/p\u003e \u003cp\u003e9.5 Conclusions 266\u003c\/p\u003e \u003cp\u003eReferences 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 269\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJim E. Riviere and Jason Chittenden\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 269\u003c\/p\u003e \u003cp\u003e10.2 Principles of Dermal Absorption 270\u003c\/p\u003e \u003cp\u003e10.3 Dermal Mixtures 274\u003c\/p\u003e \u003cp\u003e10.4 Model Systems 275\u003c\/p\u003e \u003cp\u003e10.5 Local Skin Versus Systemic Endpoints 277\u003c\/p\u003e \u003cp\u003e10.6 QSAR Approaches to Model Dermal Absorption 278\u003c\/p\u003e \u003cp\u003e10.7 PharmacokineticModels 281\u003c\/p\u003e \u003cp\u003e10.8 Conclusions 284\u003c\/p\u003e \u003cp\u003eReferences 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV New Technologies for Toxicology, Future Perspectives 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Big Data in Computational Toxicology: Challenges and Opportunities 293\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLinlin Zhao and Hao Zhu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Big Data Scenario of Computational Toxicology 293\u003c\/p\u003e \u003cp\u003e11.2 Fast-Growing Chemical Toxicity Data 295\u003c\/p\u003e \u003cp\u003e11.3 The Use of Big Data Approaches in Modern Computational Toxicology 299\u003c\/p\u003e \u003cp\u003e11.3.1 Profiling the Toxicants with Massive Biological Data 299\u003c\/p\u003e \u003cp\u003e11.3.2 Read-Across Study to Fill Data Gap 301\u003c\/p\u003e \u003cp\u003e11.3.3 Unstructured Data Curation 302\u003c\/p\u003e \u003cp\u003e11.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts 303\u003c\/p\u003e \u003cp\u003eReferences 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 HLA-Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular\u003c\/b\u003e \u003cb\u003eModeling 313\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGeorge van Den Driessche and Denis Fourches\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 313\u003c\/p\u003e \u003cp\u003e12.2 Human Leukocyte Antigens 314\u003c\/p\u003e \u003cp\u003e12.2.1 HLA Proteins 314\u003c\/p\u003e \u003cp\u003e12.2.2 ADR–HLA Associations 316\u003c\/p\u003e \u003cp\u003e12.2.3 HLA-Drug-Peptide Proposed T-Cell Signaling Mechanisms 321\u003c\/p\u003e \u003cp\u003e12.3 Structure-Based Molecular Docking to Study HLA-Mediated ADRs 322\u003c\/p\u003e \u003cp\u003e12.3.1 Structure-Based Docking 324\u003c\/p\u003e \u003cp\u003e12.3.2 Case Study: Abacavir with B*57:01 326\u003c\/p\u003e \u003cp\u003e12.3.3 Limitations 332\u003c\/p\u003e \u003cp\u003e12.4 Perspectives 334\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Open Science Data Repository for Toxicology 341\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eValery Tkachenko, Richard Zakharov, and Sean Ekins\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 341\u003c\/p\u003e \u003cp\u003e13.2 Open Science Data Repository 342\u003c\/p\u003e \u003cp\u003e13.3 Benefits of OSDR 344\u003c\/p\u003e \u003cp\u003e13.3.1 Chemically and Semantically Enabled Scientific Data Repository 344\u003c\/p\u003e \u003cp\u003e13.3.2 Chemical Validation and Standardization Platform 346\u003c\/p\u003e \u003cp\u003e13.3.3 Format Adapters 347\u003c\/p\u003e \u003cp\u003e13.3.4 Open Platform for Data Acquisition, Curation, and Dissemination 350\u003c\/p\u003e \u003cp\u003e13.3.5 Dataledger 350\u003c\/p\u003e \u003cp\u003e13.4 Technical Details 351\u003c\/p\u003e \u003cp\u003e13.5 FutureWork 353\u003c\/p\u003e \u003cp\u003e13.5.1 Implementation of Ontology-Based Properties 356\u003c\/p\u003e \u003cp\u003e13.5.2 Implementation of an Advanced Search System 357\u003c\/p\u003e \u003cp\u003e13.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework 357\u003c\/p\u003e \u003cp\u003eReferences 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Developing Next Generation Tools for Computational Toxicology 363\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlex M. Clark, Kimberley M. Zorn, Mary A. Lingerfelt, and Sean Ekins\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 363\u003c\/p\u003e \u003cp\u003e14.2 Developing Apps for Chemistry 364\u003c\/p\u003e \u003cp\u003e14.3 Green Chemistry 364\u003c\/p\u003e \u003cp\u003e14.3.1 Green Solvents and Lab Solvents 367\u003c\/p\u003e \u003cp\u003e14.3.2 Green Lab Notebook 370\u003c\/p\u003e \u003cp\u003e14.4 Polypharma and Assay Central 374\u003c\/p\u003e \u003cp\u003e14.4.1 Future Efforts with Assay Central 380\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 382\u003c\/p\u003e \u003cp\u003eAcknowledgments 383\u003c\/p\u003e \u003cp\u003eReferences 383\u003c\/p\u003e \u003cp\u003eIndex 389\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49528852021591,"sku":"9781119282563","price":140.55,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119282563.jpg?v=1731873277","url":"https:\/\/bookcurl.com\/products\/computational-toxicology-9781119282563","provider":"Book Curl","version":"1.0","type":"link"}