Chemometrics and Cheminformatics in Aquatic Toxicology

Preface xxi Part I Introduction 1 1 Water Quality and Contaminants of Emerging Concern (CECs) 3 Antonio Juan García-Fernández, Silvia Espín, Pilar Gómez-Ramírez, Pablo Sánchez-Virosta, and Isabel Navas 1.1 Introduction: Water Quality and Emerging Contaminants 3 1.2 Contaminants of Emerging Concern 6 1.3 Summary and Recommendations for Future Research 14 References 14 2 The Effects of Contaminants of Emerging Concern on Water Quality 23 Heiko L. Schoenfuss 2.1 Introduction 23 2.2 Assessing the Effects of CECs in Aquatic Life 27 2.3 Multiple Stressors 34 2.4 Conclusions 35 Acknowledgments 35 References 35 3 Chemometrics: Multivariate Statistical Analysis of Analytical Chemical and Biomolecular Data 45 Richard G. Brereton 3.1 Introduction 45 3.2 Historic Origins 45 3.3 Applied Statistics 46 3.4 Analytical and Physical Chemistry 48 3.5 Scientific Computing 49 3.6 Development from the 1980s 50 3.7 A Review of the Main Methods 52 3.8 Experimental Design 52 3.9 Principal Components Analysis and Pattern Recognition 53 3.10 Multivariate Signal Analysis 54 3.11 Multivariate Calibration 55 3.12 Digital Signal Processing and Time Series Analysis 56 3.13 Multiway Methods 56 3.14 Conclusion 56 References 57 4 An Introduction to Chemometrics and Cheminformatics 61 Chanin Nantasenamat 4.1 Brief History of Chemometrics/Cheminformatics 61 4.2 Current State of Cheminformatics 62 4.3 Common Cheminformatics Tasks 62 4.4 Cheminformatics Toolbox 63 4.5 Conclusion 65 References 65 Part II Chemometric and Cheminformatic Tools and Protocols 69 5 An Introduction to Some Basic Chemometric Tools 71 Lennart Eriksson, Erik Johansson, and Johan Trygg 5.1 Introduction 71 5.2 Example Datasets 72 5.3 Data Analytical Methods 73 5.4 Results 78 5.5 Discussion 85 References 87 6 From Data to Models: Mining Experimental Values with Machine Learning Tools 89 Giuseppina Gini and Emilio Benfenati 6.1 Introduction 89 6.2 Data and Models 91 6.3 Basic Methods in Model Development with ML 94 6.4 More Advanced ML Methodologies 103 6.5 Deep Learning 113 6.6 Conclusions 120 References 121 7 Machine Learning Approaches in Computational Toxicology Studies 125 Pravin Ambure, Stephen J. Barigye, and Rafael Gozalbes 7.1 Introduction 125 7.2 Toxicity Data Set Preparation 127 7.3 Machine-Learning Techniques 128 7.4 Model Evaluation 145 7.5 Freely Available Software Tools and Open-Source Libraries Relevant to Machine Learning 146 7.6 Concluding Remarks 148 Acknowledgment 148 References 148 8 Counter-Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity 157 Viktor Drgan and Marjan Vračko 8.1 Introduction 157 8.2 Examples of Counter-Propagation Artificial Neural Networks in Fish Toxicity Modeling 158 8.3 Counter-Propagation Artificial Neural Networks 163 8.4 Conclusions 164 References 164 9 Aiming High versus Aiming All: Aquatic Toxicology and QSAR Multitarget Models 167 Ana S. Moura and M. Natália D. S. Cordeiro 9.1 Introduction 167 9.2 Multitarget QSARS and Aquatic Toxicology 168 9.3 Biotargets and Aqueous Environmental Assessment: Solutions and Recommendations 175 9.4 Future Perspectives and Conclusion 175 References 176 10 Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity 181 S. Raimondo, C.M. Lavelle, and M.G. Barron 10.1 Introduction 181 10.2 Acute Toxicity Estimation 183 10.3 Sublethal Toxicity Extrapolation 186 10.4 Discussion 191 10.5 Conclusions 192 Disclaimer 192 References 193 Part III Case Studies and Literature Reports 201 11 The QSAR Paradigm to Explore and Predict Aquatic Toxicity 203 Fotios Tsopelas and Anna Tsantili-Kakoulidou 11.1 Introduction 203 11.2 Application of QSAR Methodology to Predict Aquatic Toxicity 204 11.3 QSAR for Narcosis – The Impact of Hydrophobicity 209 11.4 Excess Toxicity – Overview 213 11.5 Predictions of Bioconcentration Factor 216 11.6 Conclusions 218 References 219 12 Application of Cheminformatics to Model Fish Toxicity 227 Sorin Avram, Simona Funar-Timofei, and Gheorghe Ilia 12.1 Introduction 227 12.2 Fish Toxicities 228 12.3 Toxicity in Fish Families and Species 229 12.4 The Fathead Minnow, the Rainbow Trout, and the Bluegill 231 12.5 Toxicity Variations in FIT Compounds 232 12.6 Modeling Wide-Range Toxicity Compounds 233 12.7 Further Evaluations 236 12.8 Alternative Approaches 237 12.9 Mechanisms of Action 238 12.10 Conclusions 239 Acknowledgments 239 Abbreviations List 239 References 240 13 Chemometric Modeling of Algal and Daphnia Toxicity 243 Luminita Crisan, Ana Borota, Alina Bora, Simona Funar-Timofei, and Gheorghe Ilia 13.1 Introduction 243 13.2 Algae Class 247 13.3 Daphniidae Family 256 13.4 Interspecies Correlation Estimation for Algal and Daphnia Aquatic Toxicity 262 13.5 Conclusions 267 Abbreviations List 268 References 268 14 Chemometric Modeling of Algal Toxicity 275 Melek Türker Saçan, Serli Önlü, and Gulcin Tugcu 14.1 Introduction 275 14.2 Criteria Set for the Comparison of Selected QSAR Models 277 14.3 Literature MLR Studies on Algae 283 14.4 Conclusion 288 References 289 15 Chemometric Modeling of Daphnia Toxicity 293 Amit Kumar Halder and Maria Natália Dias Soeiro Cordeiro 15.1 Introduction 293 15.2 QSTR and QSTTR Analyses 294 15.3 QSTR/QSTT/QSTTR Modeling of Daphnia Toxicity 295 15.4 Mechanistic Interpretations of Chemometric Models 309 15.5 Conclusive Remarks and Future Directions 310 Acknowledgment 311 References 311 16 Chemometric Modeling of Daphnia Toxicity: Quantum-Mechanical Insights 319 Reenu and Vikas 16.1 Introduction 319 16.2 Quantum-Mechanical Methods 321 16.3 Quantum-Mechanical Descriptors for Daphnia Toxicity 323 16.4 Concluding Remarks and Future Outlook 325 References 326 17 Chemometric Modeling of Toxicity of Chemicals to Tadpoles 331 Kabiruddin Khan and Kunal Roy 17.1 Introduction 331 17.2 Overview and Morphology of Tadpoles 332 17.3 Reports of Tadpole Toxicity Due Various Environmental Contaminants: What Do We Know So Far? 340 17.4 In silico Models Reported for Tadpole Ecotoxicity: A Literature Review 341 17.5 Application of QSARs or Related Approaches in Modeling Tadpole Toxicity: A Future Perspective 351 17.6 Conclusion 351 Acknowledgment 351 References 352 18 Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria 359 Kabiruddin Khan and Kunal Roy 18.1 Introduction 359 18.2 Marine Bacteria and Their Role in Nitrogen Fixing 360 18.3 Marine Bacteria as Biomarkers for Ecotoxicity Estimation 362 18.4 Chemometric Tools Applied in Ecotoxicity Evaluation of Marine Bacteria 363 18.5 Conclusion 373 Acknowledgment 373 References 374 19 Chemometric Modeling of Pesticide Aquatic Toxicity 377 Alina Bora and Simona Funar-Timofei 19.1 Introduction 377 19.2 QSARs Models 380 19.3 Conclusions 386 Abbreviations List 386 References 387 20 Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State-of-the-Art 391 Mabrouk Hamadache, Abdeltif Amrane, Othmane Benkortbi, and Salah Hanini 20.1 Introduction 391 20.2 Definition and Classification 391 20.3 Advantage of Aquatic Plants 392 20.4 Contaminants and Their Toxicity 394 20.5 Chemometrics for Aquatic Plants Toxicity 400 20.6 Review of Literature on Chemometrics for Aquatic Plants Toxicity 400 20.7 Conclusions 406 References 407 21 Application of 3D-QSAR Approaches to Classification and Prediction of Aquatic Toxicity 417 Sehan Lee and Mace G. Barron 21.1 Introduction 417 21.2 Principles of CAPLI 3D-QSAR 419 21.3 Applications in Chemical Classification and Toxicity Prediction 426 21.4 Limitation and Potential Improvement 429 21.5 Conclusions and Recommendations 430 Acknowledgments 430 References 430 22 QSAR Modeling of Aquatic Toxicity of Cationic Polymers 433 Hans Sanderson, Pathan M. Khan, Supratik Kar, Kunal Roy, Anna M.B. Hansen, Kristin Connors, and Scott Belanger 22.1 Introduction 433 22.2 Materials and Methods 434 22.3 Results and Discussion 440 22.4 Conclusions 450 Acknowledgments 450 References 451 Part IV Tools and Databases 453 23 In Silico Platforms for Predictive Ecotoxicology: From Machine Learning to Deep Learning 455 Yong Oh Lee and Baeckkyoung Sung 23.1 Introduction 455 23.2 Machine Learning and Deep Learning 456 23.3 Toxicity Prediction Modeling 458 23.4 Challenges and Future Directions 463 References 464 24 The Use and Evolution of Web Tools for Aquatic Toxicology Studies 473 Renata P. B. Menezes, Natália F. Sousa, Luana de Morais e Silva, Luciana Scotti, Wilton Silva Lopes, and Marcus T. Scotti 24.1 Introduction 473 24.2 Methodologies Used in Aquatic Toxicology Tests 474 24.3 Web Tools Used in Aquatic Toxicology 482 24.4 Perspectives 487 References 488 25 The Tools for Aquatic Toxicology within the VEGAHUB System 493 Emilio Benfenati, Anna Lombardo, Viktor Drgan, Marjana Novič, and Alberto Manganaro 25.1 Introduction 493 25.2 The VEGA Models 495 25.3 ToxRead and Read-Across Within VEGAHUB 505 25.4 Prometheus and JANUS 506 25.5 The Future Developments 508 25.6 Conclusions 509 References 510 26 Aquatic Toxicology Databases 513 Supratik Kar and Jerzy Leszczynski 26.1 Introduction 513 26.2 Aquatic Toxicity 514 26.3 Importance of Aquatic Toxicity Databases 516 26.4 Characteristic of an Ideal Aquatic Toxicity Database 516 26.5 Aquatic Toxicology Databases 516 26.6 Overview and Conclusion 524 Acknowledgments 524 Conflicts of Interest 525 References 525 27 Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern: The LIFE-COMBASE Project 527 María Blázquez, Oscar Andreu-Sánchez, Arantxa Ballesteros, María Luisa Fernández-Cruz, Carlos Fito, Sergi Gómez-Ganau, Rafael Gozalbes, David Hernández-Moreno, Jesus Vicente de Julián-Ortiz, Anna Lombardo, Marco Marzo, Irati Ranero, Nuria Ruiz-Costa, Jose Vicente Tarazona-Díez, and Emilio Benfenati 27.1 Introduction 527 27.2 Database Compilation 530 27.3 Development of the QSAR Models 531 27.4 Prediction of Metabolites and their Associated Toxicity 533 27.5 Implementation of the In Silico QSARs Within VEGA and Integration with Read Across Models in ToxRead 534 27.6 Implementation of the LIFE-COMBASE Decision Support System 537 27.7 Implementation of the LIFE-COMBASE Mobile App 543 27.8 Concluding Remarks 543 Acknowledgments 544 References 544 28 Image Analysis and Deep Learning Web Services for Nano informatics 547 Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Pantelis Karatzas, Philip Doganis, Dimitra-Danai Varsou, Haralambos Sarimveis, Laura-Jayne A. Ellis, Eugenia Valsami-Jones, Iseult Lynch, and Georgia Melagraki 27.1 Introduction 547 27.2 NanoXtract 549 27.3 DeepDaph 556 27.4 Conclusions 560 Acknowledgments 561 References 561 Index 565