Chemometrics and Cheminformatics in Aquatic Toxicology

Kunal Roy, PhD, is Professor in the Department of Pharmaceutical Technology in Jadavpur University in Kolkata, India. He is a recipient of the Commonwealth Academic Staff Fellowship and the Marie Curie International Incoming Fellowship. His research focus is on the quantitative structure-activity relationship and chemometric modeling, with applications in drug design and ecotoxicological modeling.

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