Chemometrics and Cheminformatics in Aquatic Toxicology
1. Auflage Januar 2022
592 Seiten, Hardcover
Praktikerbuch
ISBN:
978-1-119-68159-5
John Wiley & Sons
Das Buch Chemometrics and Cheminformatics in Aquatic Toxicology befasst sich mit den bestehenden und neu auftretenden Problemen der Verschmutzung der aquatischen Umwelt durch verschiedene metallische und organische Schadstoffe, insbesondere Industriechemikalien, Pharmazeutika, Kosmetika, Biozide, Nanomaterialien, Pestizide, Tenside, Farbstoffe und viele weitere. Es werden verschiedene chemometrische und cheminformatische Instrumente für Laien beschrieben mitsamt ihrer Anwendung auf die Analyse und Modellierung der Toxizitätsdaten von Chemikalien in Bezug auf unterschiedliche aquatische Organismen.
Eine Reihe von Datenbanken zur aquatischen Toxizität sowie chemometrische Softwaretools und Webserver werden vorgestellt und praktische Beispiele für die Modellentwicklung gegeben, einschließlich der entsprechenden Abbildungen. Darüber hinaus enthält das Werk Fallstudien und Literaturberichte, um das Verständnis des Themas abzurunden. Außerdem lernen die Leserinnen und Leser Werkzeuge und Protokolle wie maschinelles Lernen, Data Mining sowie Methoden des QSAR-basierten und ligandenbasierten chemischen Designs kennen.
Darüber hinaus bietet das Werk:
* Eine umfassende Einführung in chemometrische und cheminformatische Instrumente und Techniken, insbesondere maschinelles Lernen und Data Mining
* Eine Darstellung von Datenbanken zur aquatischen Toxizität, chemometrischen Softwaretools und Webservern
* Praktische Beispiele und Fallstudien zur Verdeutlichung und Veranschaulichung der im Buch enthaltenen Konzepte
* Eine kompakte Erläuterung der chemometrischen und cheminformatischen Instrumente sowie ihrer Anwendung auf die Analyse und Modellierung von Toxizitätsdaten
Chemometrics and Cheminformatics in Aquatic Toxicology ist ideal für Forschende und Studierende der Chemie sowie der Umwelt- und Pharmawissenschaften und sollte auch in den Bibliotheken von Fachleuten in der chemischen Industrie sowie Aufsichtsbehörden, die sich mit Chemometrie beschäftigen, einen Platz finden.
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 Vra ko
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 Novi , 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
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 Vra ko
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 Novi , 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
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.
