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List of Contributors xvii Preface xxi Acknowledgments xxiii Part I Computational Methods 1 1 AccessibleMachine Learning Approaches for Toxicology 3Sean 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 31Jakub 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 71Vinicius 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 93Ni 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 121Daniela Schuster 5.1 Introduction 121 5.2 Antitarget Screening 125 5.3 Prediction of Liver Toxicity 125 5.4 Prediction of Cardiovascular Toxicity 127 5.5 Prediction of Central Nervous System (CNS) Toxicity 128 5.6 Prediction of Endocrine Disruption 130 5.7 Prediction of ADME 135 5.8 General Remarks on the Limits and Future Perspectives for Employing Pharmacophore Models in Toxicological Studies 136 References 137 6 Transporters in Hepatotoxicity 145Eleni Kotsampasakou, Sankalp Jain, Daniela Digles, and Gerhard F. Ecker 6.1 Introduction 145 6.2 Basolateral Transporters 146 6.3 Canalicular Transporters 148 6.4 Data Sources for Transporters in Hepatotoxicity 148 6.5 In Silico Transporters Models 150 6.6 Ligand–Based Approaches 150 6.7 OATP1B1 and OATP1B3 150 6.8 NTCP 154 6.9 OCT1 154 6.10 OCT2 154 6.11 MRP1, MRP3, and MRP4 155 6.12 BSEP 155 6.13 MRP2 156 6.14 MDR1/P–gp 156 6.15 MDR3 157 6.16 BCRP 157 6.17 MATE1 158 6.18 ASBT 159 6.19 Structure–Based Approaches 159 6.20 Complex Models Incorporating Transporter Information 160 6.21 In Vitro Models 160 6.22 Multiscale Models 161 6.23 Outlook 162 Acknowledgments 164 References 164 7 Cheminformatics in a Clinical Setting 175Matthew D. Krasowski and Sean Ekins 7.1 Introduction 175 7.2 Similarity Analysis Applied to Drug of Abuse/Toxicology Immunoassays 177 7.3 Similarity Analysis Applied toTherapeutic Drug Monitoring Immunoassays 187 7.4 Similarity Analysis Applied to Steroid Hormone Immunoassays 191 7.5 Cheminformatics Applied to "Designer Drugs" 195 7.6 Relevance to Antibody–Ligand Interactions 202 7.7 Conclusions and Future Directions 203 Acknowledgment 204 References 204 Part III Applying Computers to Toxicology Assessment: Environmental and Regulatory Perspectives 211 8 Computational Tools for ADMET Profiling 213Denis Fourches, Antony J.Williams, Grace Patlewicz, Imran Shah, Chris Grulke, JohnWambaugh, Ann Richard, and Alexander Tropsha 8.1 Introduction 213 8.2 Cheminformatics Approaches for ADMET Profiling 214 8.2.1 Chemical Data Curation Prior to ADMET Modeling 215 8.2.2 QSAR Modelability Index 217 8.2.3 Predictive QSAR Model DevelopmentWorkflow 218 8.2.4 Hybrid QSAR Modeling 220 8.2.4.1 Simple Consensus 223 8.2.4.2 Mixed Chemical and Biological Features 223 8.2.4.3 Two–Step HierarchicalWorkflow 224 8.2.5 Chemical Biological Read–Across 226 8.2.6 Public Chemotype Approach to Data–Mining 229 8.3 Unsolved Challenges in Structure Based Profiling 230 8.3.1 Biological Data Curation 231 8.3.2 Identification and Treatment of Activity and Toxicity Cliffs 233 8.3.3 In Vitro to In Vivo Continuum in the Context of AOP 233 8.4 Perspectives 234 8.4.1 Profilers on the Go with Mobile Devices 235 8.4.2 Structure–Exposure–Activity Relationships 236 8.5 Conclusions 237 Acknowledgments 237 Disclaimer 237 References 238 9 Computational Toxicology and Reach 245Emilio Enfenati, Anna Lombardo, and Alessandra Roncaglioni 9.1 A Theoretical and Historical Introduction to the Evolution Toward Predictive Models 245 9.2 Reach and the Other Legislations 247 9.3 Annex XI of Reach for QSARModels 248 9.3.1 The First Condition of Annex XI and QMRF 249 9.3.2 The Second Condition and the Applicability Domain 251 9.3.3 TheThird Condition of Annex XI, and the Use of the QSAR Models 252 9.3.4 Adequate and Reliable Documentation of the Applied Method 254 9.4 The ECHA Guidelines and the Use of QSAR Models within ECHA 255 9.4.1 Example of Bioconcentration Factor (BCF) 255 9.4.2 Example of Mutagenicity (Reverse–Mutation Assay) Prediction 260 9.5 Conclusions 266 References 266 10 Computational Approaches to Predicting Dermal Absorption of Complex Topical Mixtures 269Jim E. Riviere and Jason Chittenden 10.1 Introduction 269 10.2 Principles of Dermal Absorption 270 10.3 Dermal Mixtures 274 10.4 Model Systems 275 10.5 Local Skin Versus Systemic Endpoints 277 10.6 QSAR Approaches to Model Dermal Absorption 278 10.7 PharmacokineticModels 281 10.8 Conclusions 284 References 285 Part IV New Technologies for Toxicology, Future Perspectives 291 11 Big Data in Computational Toxicology: Challenges and Opportunities 293Linlin Zhao and Hao Zhu 11.1 Big Data Scenario of Computational Toxicology 293 11.2 Fast–Growing Chemical Toxicity Data 295 11.3 The Use of Big Data Approaches in Modern Computational Toxicology 299 11.3.1 Profiling the Toxicants with Massive Biological Data 299 11.3.2 Read–Across Study to Fill Data Gap 301 11.3.3 Unstructured Data Curation 302 11.4 Challenges of Big Data Research in Computational Toxicology and Relevant Forecasts 303 References 304 12 HLA–Mediated Adverse Drug Reactions: Challenges and Opportunities for Predictive Molecular Modeling 313George van Den Driessche and Denis Fourches 12.1 Introduction 313 12.2 Human Leukocyte Antigens 314 12.2.1 HLA Proteins 314 12.2.2 ADR–HLA Associations 316 12.2.3 HLA–Drug–Peptide Proposed T–Cell Signaling Mechanisms 321 12.3 Structure–Based Molecular Docking to Study HLA–Mediated ADRs 322 12.3.1 Structure–Based Docking 324 12.3.2 Case Study: Abacavir with B∗57:01 326 12.3.3 Limitations 332 12.4 Perspectives 334 References 335 13 Open Science Data Repository for Toxicology 341Valery Tkachenko, Richard Zakharov, and Sean Ekins 13.1 Introduction 341 13.2 Open Science Data Repository 342 13.3 Benefits of OSDR 344 13.3.1 Chemically and Semantically Enabled Scientific Data Repository 344 13.3.2 Chemical Validation and Standardization Platform 346 13.3.3 Format Adapters 347 13.3.4 Open Platform for Data Acquisition, Curation, and Dissemination 350 13.3.5 Dataledger 350 13.4 Technical Details 351 13.5 FutureWork 353 13.5.1 Implementation of Ontology–Based Properties 356 13.5.2 Implementation of an Advanced Search System 357 13.5.3 Implementation of a Scientist Profile, Advanced Security, Data Sharing Capabilities and Notifications Framework 357 References 358 14 Developing Next Generation Tools for Computational Toxicology 363Alex M. Clark, Kimberley M. Zorn, Mary A. Lingerfelt, and Sean Ekins 14.1 Introduction 363 14.2 Developing Apps for Chemistry 364 14.3 Green Chemistry 364 14.3.1 Green Solvents and Lab Solvents 367 14.3.2 Green Lab Notebook 370 14.4 Polypharma and Assay Central 374 14.4.1 Future Efforts with Assay Central 380 14.5 Conclusion 382 Acknowledgments 383 References 383 Index 389