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.

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• Preface xxiPart I Introduction 11 Water Quality and Contaminants of Emerging Concern (CECs) 3Antonio Juan García-Fernández, Silvia Espín, Pilar Gómez-Ramírez, Pablo Sánchez-Virosta, and Isabel Navas1.1 Introduction: Water Quality and Emerging Contaminants 31.2 Contaminants of Emerging Concern 61.3 Summary and Recommendations for Future Research 14References 142 The Effects of Contaminants of Emerging Concern on Water Quality 23Heiko L. Schoenfuss2.1 Introduction 232.2 Assessing the Effects of CECs in Aquatic Life 272.3 Multiple Stressors 342.4 Conclusions 35Acknowledgments 35References 353 Chemometrics: Multivariate Statistical Analysis of Analytical Chemical and Biomolecular Data 45Richard G. Brereton3.1 Introduction 453.2 Historic Origins 453.3 Applied Statistics 463.4 Analytical and Physical Chemistry 483.5 Scientific Computing 493.6 Development from the 1980s 503.7 A Review of the Main Methods 523.8 Experimental Design 523.9 Principal Components Analysis and Pattern Recognition 533.10 Multivariate Signal Analysis 543.11 Multivariate Calibration 553.12 Digital Signal Processing and Time Series Analysis 563.13 Multiway Methods 563.14 Conclusion 56References 574 An Introduction to Chemometrics and Cheminformatics 61Chanin Nantasenamat4.1 Brief History of Chemometrics/Cheminformatics 614.2 Current State of Cheminformatics 624.3 Common Cheminformatics Tasks 624.4 Cheminformatics Toolbox 634.5 Conclusion 65References 65Part II Chemometric and Cheminformatic Tools and Protocols 695 An Introduction to Some Basic Chemometric Tools 71Lennart Eriksson, Erik Johansson, and Johan Trygg5.1 Introduction 715.2 Example Datasets 725.3 Data Analytical Methods 735.4 Results 785.5 Discussion 85References 876 From Data to Models: Mining Experimental Values with Machine Learning Tools 89Giuseppina Gini and Emilio Benfenati6.1 Introduction 896.2 Data and Models 916.3 Basic Methods in Model Development with ML 946.4 More Advanced ML Methodologies 1036.5 Deep Learning 1136.6 Conclusions 120References 1217 Machine Learning Approaches in Computational Toxicology Studies 125Pravin Ambure, Stephen J. Barigye, and Rafael Gozalbes7.1 Introduction 1257.2 Toxicity Data Set Preparation 1277.3 Machine-Learning Techniques 1287.4 Model Evaluation 1457.5 Freely Available Software Tools and Open-Source Libraries Relevant to Machine Learning 1467.6 Concluding Remarks 148Acknowledgment 148References 1488 Counter-Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity 157Viktor Drgan and Marjan Vračko8.1 Introduction 1578.2 Examples of Counter-Propagation Artificial Neural Networks in Fish Toxicity Modeling 1588.3 Counter-Propagation Artificial Neural Networks 1638.4 Conclusions 164References 1649 Aiming High versus Aiming All: Aquatic Toxicology and QSAR Multitarget Models 167Ana S. Moura and M. Natália D. S. Cordeiro9.1 Introduction 1679.2 Multitarget QSARS and Aquatic Toxicology 1689.3 Biotargets and Aqueous Environmental Assessment: Solutions and Recommendations 1759.4 Future Perspectives and Conclusion 175References 17610 Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity 181S. Raimondo, C.M. Lavelle, and M.G. Barron10.1 Introduction 18110.2 Acute Toxicity Estimation 18310.3 Sublethal Toxicity Extrapolation 18610.4 Discussion 19110.5 Conclusions 192Disclaimer 192References 193Part III Case Studies and Literature Reports 20111 The QSAR Paradigm to Explore and Predict Aquatic Toxicity 203Fotios Tsopelas and Anna Tsantili-Kakoulidou11.1 Introduction 20311.2 Application of QSAR Methodology to Predict Aquatic Toxicity 20411.3 QSAR for Narcosis – The Impact of Hydrophobicity 20911.4 Excess Toxicity – Overview 21311.5 Predictions of Bioconcentration Factor 21611.6 Conclusions 218References 21912 Application of Cheminformatics to Model Fish Toxicity 227Sorin Avram, Simona Funar-Timofei, and Gheorghe Ilia12.1 Introduction 22712.2 Fish Toxicities 22812.3 Toxicity in Fish Families and Species 22912.4 The Fathead Minnow, the Rainbow Trout, and the Bluegill 23112.5 Toxicity Variations in FIT Compounds 23212.6 Modeling Wide-Range Toxicity Compounds 23312.7 Further Evaluations 23612.8 Alternative Approaches 23712.9 Mechanisms of Action 23812.10 Conclusions 239Acknowledgments 239Abbreviations List 239References 24013 Chemometric Modeling of Algal and Daphnia Toxicity 243Luminita Crisan, Ana Borota, Alina Bora, Simona Funar-Timofei, and Gheorghe Ilia13.1 Introduction 24313.2 Algae Class 24713.3 Daphniidae Family 25613.4 Interspecies Correlation Estimation for Algal and Daphnia Aquatic Toxicity 26213.5 Conclusions 267Abbreviations List 268References 26814 Chemometric Modeling of Algal Toxicity 275Melek Türker Saçan, Serli Önlü, and Gulcin Tugcu14.1 Introduction 27514.2 Criteria Set for the Comparison of Selected QSAR Models 27714.3 Literature MLR Studies on Algae 28314.4 Conclusion 288References 28915 Chemometric Modeling of Daphnia Toxicity 293Amit Kumar Halder and Maria Natália Dias Soeiro Cordeiro15.1 Introduction 29315.2 QSTR and QSTTR Analyses 29415.3 QSTR/QSTT/QSTTR Modeling of Daphnia Toxicity 29515.4 Mechanistic Interpretations of Chemometric Models 30915.5 Conclusive Remarks and Future Directions 310Acknowledgment 311References 31116 Chemometric Modeling of Daphnia Toxicity: Quantum-Mechanical Insights 319Reenu and Vikas16.1 Introduction 31916.2 Quantum-Mechanical Methods 32116.3 Quantum-Mechanical Descriptors for Daphnia Toxicity 32316.4 Concluding Remarks and Future Outlook 325References 32617 Chemometric Modeling of Toxicity of Chemicals to Tadpoles 331Kabiruddin Khan and Kunal Roy17.1 Introduction 33117.2 Overview and Morphology of Tadpoles 33217.3 Reports of Tadpole Toxicity Due Various Environmental Contaminants: What Do We Know So Far? 34017.4 In silico Models Reported for Tadpole Ecotoxicity: A Literature Review 34117.5 Application of QSARs or Related Approaches in Modeling Tadpole Toxicity: A Future Perspective 35117.6 Conclusion 351Acknowledgment 351References 35218 Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria 359Kabiruddin Khan and Kunal Roy18.1 Introduction 35918.2 Marine Bacteria and Their Role in Nitrogen Fixing 36018.3 Marine Bacteria as Biomarkers for Ecotoxicity Estimation 36218.4 Chemometric Tools Applied in Ecotoxicity Evaluation of Marine Bacteria 36318.5 Conclusion 373Acknowledgment 373References 37419 Chemometric Modeling of Pesticide Aquatic Toxicity 377Alina Bora and Simona Funar-Timofei19.1 Introduction 37719.2 QSARs Models 38019.3 Conclusions 386Abbreviations List 386References 38720 Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State-of-the-Art 391Mabrouk Hamadache, Abdeltif Amrane, Othmane Benkortbi, and Salah Hanini20.1 Introduction 39120.2 Definition and Classification 39120.3 Advantage of Aquatic Plants 39220.4 Contaminants and Their Toxicity 39420.5 Chemometrics for Aquatic Plants Toxicity 40020.6 Review of Literature on Chemometrics for Aquatic Plants Toxicity 40020.7 Conclusions 406References 40721 Application of 3D-QSAR Approaches to Classification and Prediction of Aquatic Toxicity 417Sehan Lee and Mace G. Barron21.1 Introduction 41721.2 Principles of CAPLI 3D-QSAR 41921.3 Applications in Chemical Classification and Toxicity Prediction 42621.4 Limitation and Potential Improvement 42921.5 Conclusions and Recommendations 430Acknowledgments 430References 43022 QSAR Modeling of Aquatic Toxicity of Cationic Polymers 433Hans Sanderson, Pathan M. Khan, Supratik Kar, Kunal Roy, Anna M.B. Hansen, Kristin Connors, and Scott Belanger22.1 Introduction 43322.2 Materials and Methods 43422.3 Results and Discussion 44022.4 Conclusions 450Acknowledgments 450References 451Part IV Tools and Databases 45323 In Silico Platforms for Predictive Ecotoxicology: From Machine Learning to Deep Learning 455Yong Oh Lee and Baeckkyoung Sung23.1 Introduction 45523.2 Machine Learning and Deep Learning 45623.3 Toxicity Prediction Modeling 45823.4 Challenges and Future Directions 463References 46424 The Use and Evolution of Web Tools for Aquatic Toxicology Studies 473Renata P. B. Menezes, Natália F. Sousa, Luana de Morais e Silva, Luciana Scotti, Wilton Silva Lopes, and Marcus T. Scotti24.1 Introduction 47324.2 Methodologies Used in Aquatic Toxicology Tests 47424.3 Web Tools Used in Aquatic Toxicology 48224.4 Perspectives 487References 48825 The Tools for Aquatic Toxicology within the VEGAHUB System 493Emilio Benfenati, Anna Lombardo, Viktor Drgan, Marjana Novič, and Alberto Manganaro25.1 Introduction 49325.2 The VEGA Models 49525.3 ToxRead and Read-Across Within VEGAHUB 50525.4 Prometheus and JANUS 50625.5 The Future Developments 50825.6 Conclusions 509References 51026 Aquatic Toxicology Databases 513Supratik Kar and Jerzy Leszczynski26.1 Introduction 51326.2 Aquatic Toxicity 51426.3 Importance of Aquatic Toxicity Databases 51626.4 Characteristic of an Ideal Aquatic Toxicity Database 51626.5 Aquatic Toxicology Databases 51626.6 Overview and Conclusion 524Acknowledgments 524Conflicts of Interest 525References 52527 Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern: The LIFE-COMBASE Project 527María 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 Benfenati27.1 Introduction 52727.2 Database Compilation 53027.3 Development of the QSAR Models 53127.4 Prediction of Metabolites and their Associated Toxicity 53327.5 Implementation of the In Silico QSARs Within VEGA and Integration with Read Across Models in ToxRead 53427.6 Implementation of the LIFE-COMBASE Decision Support System 53727.7 Implementation of the LIFE-COMBASE Mobile App 54327.8 Concluding Remarks 543Acknowledgments 544References 54428 Image Analysis and Deep Learning Web Services for Nano informatics 547Anastasios 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 Melagraki27.1 Introduction 54727.2 NanoXtract 54927.3 DeepDaph 55627.4 Conclusions 560Acknowledgments 561References 561Index 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.

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• Preface xxiPart I Introduction 11 Water Quality and Contaminants of Emerging Concern (CECs) 3Antonio Juan García-Fernández, Silvia Espín, Pilar Gómez-Ramírez, Pablo Sánchez-Virosta, and Isabel Navas1.1 Introduction: Water Quality and Emerging Contaminants 31.2 Contaminants of Emerging Concern 61.3 Summary and Recommendations for Future Research 14References 142 The Effects of Contaminants of Emerging Concern on Water Quality 23Heiko L. Schoenfuss2.1 Introduction 232.2 Assessing the Effects of CECs in Aquatic Life 272.3 Multiple Stressors 342.4 Conclusions 35Acknowledgments 35References 353 Chemometrics: Multivariate Statistical Analysis of Analytical Chemical and Biomolecular Data 45Richard G. Brereton3.1 Introduction 453.2 Historic Origins 453.3 Applied Statistics 463.4 Analytical and Physical Chemistry 483.5 Scientific Computing 493.6 Development from the 1980s 503.7 A Review of the Main Methods 523.8 Experimental Design 523.9 Principal Components Analysis and Pattern Recognition 533.10 Multivariate Signal Analysis 543.11 Multivariate Calibration 553.12 Digital Signal Processing and Time Series Analysis 563.13 Multiway Methods 563.14 Conclusion 56References 574 An Introduction to Chemometrics and Cheminformatics 61Chanin Nantasenamat4.1 Brief History of Chemometrics/Cheminformatics 614.2 Current State of Cheminformatics 624.3 Common Cheminformatics Tasks 624.4 Cheminformatics Toolbox 634.5 Conclusion 65References 65Part II Chemometric and Cheminformatic Tools and Protocols 695 An Introduction to Some Basic Chemometric Tools 71Lennart Eriksson, Erik Johansson, and Johan Trygg5.1 Introduction 715.2 Example Datasets 725.3 Data Analytical Methods 735.4 Results 785.5 Discussion 85References 876 From Data to Models: Mining Experimental Values with Machine Learning Tools 89Giuseppina Gini and Emilio Benfenati6.1 Introduction 896.2 Data and Models 916.3 Basic Methods in Model Development with ML 946.4 More Advanced ML Methodologies 1036.5 Deep Learning 1136.6 Conclusions 120References 1217 Machine Learning Approaches in Computational Toxicology Studies 125Pravin Ambure, Stephen J. Barigye, and Rafael Gozalbes7.1 Introduction 1257.2 Toxicity Data Set Preparation 1277.3 Machine-Learning Techniques 1287.4 Model Evaluation 1457.5 Freely Available Software Tools and Open-Source Libraries Relevant to Machine Learning 1467.6 Concluding Remarks 148Acknowledgment 148References 1488 Counter-Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity 157Viktor Drgan and Marjan Vračko8.1 Introduction 1578.2 Examples of Counter-Propagation Artificial Neural Networks in Fish Toxicity Modeling 1588.3 Counter-Propagation Artificial Neural Networks 1638.4 Conclusions 164References 1649 Aiming High versus Aiming All: Aquatic Toxicology and QSAR Multitarget Models 167Ana S. Moura and M. Natália D. S. Cordeiro9.1 Introduction 1679.2 Multitarget QSARS and Aquatic Toxicology 1689.3 Biotargets and Aqueous Environmental Assessment: Solutions and Recommendations 1759.4 Future Perspectives and Conclusion 175References 17610 Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity 181S. Raimondo, C.M. Lavelle, and M.G. Barron10.1 Introduction 18110.2 Acute Toxicity Estimation 18310.3 Sublethal Toxicity Extrapolation 18610.4 Discussion 19110.5 Conclusions 192Disclaimer 192References 193Part III Case Studies and Literature Reports 20111 The QSAR Paradigm to Explore and Predict Aquatic Toxicity 203Fotios Tsopelas and Anna Tsantili-Kakoulidou11.1 Introduction 20311.2 Application of QSAR Methodology to Predict Aquatic Toxicity 20411.3 QSAR for Narcosis – The Impact of Hydrophobicity 20911.4 Excess Toxicity – Overview 21311.5 Predictions of Bioconcentration Factor 21611.6 Conclusions 218References 21912 Application of Cheminformatics to Model Fish Toxicity 227Sorin Avram, Simona Funar-Timofei, and Gheorghe Ilia12.1 Introduction 22712.2 Fish Toxicities 22812.3 Toxicity in Fish Families and Species 22912.4 The Fathead Minnow, the Rainbow Trout, and the Bluegill 23112.5 Toxicity Variations in FIT Compounds 23212.6 Modeling Wide-Range Toxicity Compounds 23312.7 Further Evaluations 23612.8 Alternative Approaches 23712.9 Mechanisms of Action 23812.10 Conclusions 239Acknowledgments 239Abbreviations List 239References 24013 Chemometric Modeling of Algal and Daphnia Toxicity 243Luminita Crisan, Ana Borota, Alina Bora, Simona Funar-Timofei, and Gheorghe Ilia13.1 Introduction 24313.2 Algae Class 24713.3 Daphniidae Family 25613.4 Interspecies Correlation Estimation for Algal and Daphnia Aquatic Toxicity 26213.5 Conclusions 267Abbreviations List 268References 26814 Chemometric Modeling of Algal Toxicity 275Melek Türker Saçan, Serli Önlü, and Gulcin Tugcu14.1 Introduction 27514.2 Criteria Set for the Comparison of Selected QSAR Models 27714.3 Literature MLR Studies on Algae 28314.4 Conclusion 288References 28915 Chemometric Modeling of Daphnia Toxicity 293Amit Kumar Halder and Maria Natália Dias Soeiro Cordeiro15.1 Introduction 29315.2 QSTR and QSTTR Analyses 29415.3 QSTR/QSTT/QSTTR Modeling of Daphnia Toxicity 29515.4 Mechanistic Interpretations of Chemometric Models 30915.5 Conclusive Remarks and Future Directions 310Acknowledgment 311References 31116 Chemometric Modeling of Daphnia Toxicity: Quantum-Mechanical Insights 319Reenu and Vikas16.1 Introduction 31916.2 Quantum-Mechanical Methods 32116.3 Quantum-Mechanical Descriptors for Daphnia Toxicity 32316.4 Concluding Remarks and Future Outlook 325References 32617 Chemometric Modeling of Toxicity of Chemicals to Tadpoles 331Kabiruddin Khan and Kunal Roy17.1 Introduction 33117.2 Overview and Morphology of Tadpoles 33217.3 Reports of Tadpole Toxicity Due Various Environmental Contaminants: What Do We Know So Far? 34017.4 In silico Models Reported for Tadpole Ecotoxicity: A Literature Review 34117.5 Application of QSARs or Related Approaches in Modeling Tadpole Toxicity: A Future Perspective 35117.6 Conclusion 351Acknowledgment 351References 35218 Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria 359Kabiruddin Khan and Kunal Roy18.1 Introduction 35918.2 Marine Bacteria and Their Role in Nitrogen Fixing 36018.3 Marine Bacteria as Biomarkers for Ecotoxicity Estimation 36218.4 Chemometric Tools Applied in Ecotoxicity Evaluation of Marine Bacteria 36318.5 Conclusion 373Acknowledgment 373References 37419 Chemometric Modeling of Pesticide Aquatic Toxicity 377Alina Bora and Simona Funar-Timofei19.1 Introduction 37719.2 QSARs Models 38019.3 Conclusions 386Abbreviations List 386References 38720 Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State-of-the-Art 391Mabrouk Hamadache, Abdeltif Amrane, Othmane Benkortbi, and Salah Hanini20.1 Introduction 39120.2 Definition and Classification 39120.3 Advantage of Aquatic Plants 39220.4 Contaminants and Their Toxicity 39420.5 Chemometrics for Aquatic Plants Toxicity 40020.6 Review of Literature on Chemometrics for Aquatic Plants Toxicity 40020.7 Conclusions 406References 40721 Application of 3D-QSAR Approaches to Classification and Prediction of Aquatic Toxicity 417Sehan Lee and Mace G. Barron21.1 Introduction 41721.2 Principles of CAPLI 3D-QSAR 41921.3 Applications in Chemical Classification and Toxicity Prediction 42621.4 Limitation and Potential Improvement 42921.5 Conclusions and Recommendations 430Acknowledgments 430References 43022 QSAR Modeling of Aquatic Toxicity of Cationic Polymers 433Hans Sanderson, Pathan M. Khan, Supratik Kar, Kunal Roy, Anna M.B. Hansen, Kristin Connors, and Scott Belanger22.1 Introduction 43322.2 Materials and Methods 43422.3 Results and Discussion 44022.4 Conclusions 450Acknowledgments 450References 451Part IV Tools and Databases 45323 In Silico Platforms for Predictive Ecotoxicology: From Machine Learning to Deep Learning 455Yong Oh Lee and Baeckkyoung Sung23.1 Introduction 45523.2 Machine Learning and Deep Learning 45623.3 Toxicity Prediction Modeling 45823.4 Challenges and Future Directions 463References 46424 The Use and Evolution of Web Tools for Aquatic Toxicology Studies 473Renata P. B. Menezes, Natália F. Sousa, Luana de Morais e Silva, Luciana Scotti, Wilton Silva Lopes, and Marcus T. Scotti24.1 Introduction 47324.2 Methodologies Used in Aquatic Toxicology Tests 47424.3 Web Tools Used in Aquatic Toxicology 48224.4 Perspectives 487References 48825 The Tools for Aquatic Toxicology within the VEGAHUB System 493Emilio Benfenati, Anna Lombardo, Viktor Drgan, Marjana Novič, and Alberto Manganaro25.1 Introduction 49325.2 The VEGA Models 49525.3 ToxRead and Read-Across Within VEGAHUB 50525.4 Prometheus and JANUS 50625.5 The Future Developments 50825.6 Conclusions 509References 51026 Aquatic Toxicology Databases 513Supratik Kar and Jerzy Leszczynski26.1 Introduction 51326.2 Aquatic Toxicity 51426.3 Importance of Aquatic Toxicity Databases 51626.4 Characteristic of an Ideal Aquatic Toxicity Database 51626.5 Aquatic Toxicology Databases 51626.6 Overview and Conclusion 524Acknowledgments 524Conflicts of Interest 525References 52527 Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern: The LIFE-COMBASE Project 527María 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 Benfenati27.1 Introduction 52727.2 Database Compilation 53027.3 Development of the QSAR Models 53127.4 Prediction of Metabolites and their Associated Toxicity 53327.5 Implementation of the In Silico QSARs Within VEGA and Integration with Read Across Models in ToxRead 53427.6 Implementation of the LIFE-COMBASE Decision Support System 53727.7 Implementation of the LIFE-COMBASE Mobile App 54327.8 Concluding Remarks 543Acknowledgments 544References 54428 Image Analysis and Deep Learning Web Services for Nano informatics 547Anastasios 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 Melagraki27.1 Introduction 54727.2 NanoXtract 54927.3 DeepDaph 55627.4 Conclusions 560Acknowledgments 561References 561Index 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.

Select Guide Rating

• Preface xxiPart I Introduction 11 Water Quality and Contaminants of Emerging Concern (CECs) 3Antonio Juan García-Fernández, Silvia Espín, Pilar Gómez-Ramírez, Pablo Sánchez-Virosta, and Isabel Navas1.1 Introduction: Water Quality and Emerging Contaminants 31.2 Contaminants of Emerging Concern 61.3 Summary and Recommendations for Future Research 14References 142 The Effects of Contaminants of Emerging Concern on Water Quality 23Heiko L. Schoenfuss2.1 Introduction 232.2 Assessing the Effects of CECs in Aquatic Life 272.3 Multiple Stressors 342.4 Conclusions 35Acknowledgments 35References 353 Chemometrics: Multivariate Statistical Analysis of Analytical Chemical and Biomolecular Data 45Richard G. Brereton3.1 Introduction 453.2 Historic Origins 453.3 Applied Statistics 463.4 Analytical and Physical Chemistry 483.5 Scientific Computing 493.6 Development from the 1980s 503.7 A Review of the Main Methods 523.8 Experimental Design 523.9 Principal Components Analysis and Pattern Recognition 533.10 Multivariate Signal Analysis 543.11 Multivariate Calibration 553.12 Digital Signal Processing and Time Series Analysis 563.13 Multiway Methods 563.14 Conclusion 56References 574 An Introduction to Chemometrics and Cheminformatics 61Chanin Nantasenamat4.1 Brief History of Chemometrics/Cheminformatics 614.2 Current State of Cheminformatics 624.3 Common Cheminformatics Tasks 624.4 Cheminformatics Toolbox 634.5 Conclusion 65References 65Part II Chemometric and Cheminformatic Tools and Protocols 695 An Introduction to Some Basic Chemometric Tools 71Lennart Eriksson, Erik Johansson, and Johan Trygg5.1 Introduction 715.2 Example Datasets 725.3 Data Analytical Methods 735.4 Results 785.5 Discussion 85References 876 From Data to Models: Mining Experimental Values with Machine Learning Tools 89Giuseppina Gini and Emilio Benfenati6.1 Introduction 896.2 Data and Models 916.3 Basic Methods in Model Development with ML 946.4 More Advanced ML Methodologies 1036.5 Deep Learning 1136.6 Conclusions 120References 1217 Machine Learning Approaches in Computational Toxicology Studies 125Pravin Ambure, Stephen J. Barigye, and Rafael Gozalbes7.1 Introduction 1257.2 Toxicity Data Set Preparation 1277.3 Machine-Learning Techniques 1287.4 Model Evaluation 1457.5 Freely Available Software Tools and Open-Source Libraries Relevant to Machine Learning 1467.6 Concluding Remarks 148Acknowledgment 148References 1488 Counter-Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity 157Viktor Drgan and Marjan Vračko8.1 Introduction 1578.2 Examples of Counter-Propagation Artificial Neural Networks in Fish Toxicity Modeling 1588.3 Counter-Propagation Artificial Neural Networks 1638.4 Conclusions 164References 1649 Aiming High versus Aiming All: Aquatic Toxicology and QSAR Multitarget Models 167Ana S. Moura and M. Natália D. S. Cordeiro9.1 Introduction 1679.2 Multitarget QSARS and Aquatic Toxicology 1689.3 Biotargets and Aqueous Environmental Assessment: Solutions and Recommendations 1759.4 Future Perspectives and Conclusion 175References 17610 Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity 181S. Raimondo, C.M. Lavelle, and M.G. Barron10.1 Introduction 18110.2 Acute Toxicity Estimation 18310.3 Sublethal Toxicity Extrapolation 18610.4 Discussion 19110.5 Conclusions 192Disclaimer 192References 193Part III Case Studies and Literature Reports 20111 The QSAR Paradigm to Explore and Predict Aquatic Toxicity 203Fotios Tsopelas and Anna Tsantili-Kakoulidou11.1 Introduction 20311.2 Application of QSAR Methodology to Predict Aquatic Toxicity 20411.3 QSAR for Narcosis – The Impact of Hydrophobicity 20911.4 Excess Toxicity – Overview 21311.5 Predictions of Bioconcentration Factor 21611.6 Conclusions 218References 21912 Application of Cheminformatics to Model Fish Toxicity 227Sorin Avram, Simona Funar-Timofei, and Gheorghe Ilia12.1 Introduction 22712.2 Fish Toxicities 22812.3 Toxicity in Fish Families and Species 22912.4 The Fathead Minnow, the Rainbow Trout, and the Bluegill 23112.5 Toxicity Variations in FIT Compounds 23212.6 Modeling Wide-Range Toxicity Compounds 23312.7 Further Evaluations 23612.8 Alternative Approaches 23712.9 Mechanisms of Action 23812.10 Conclusions 239Acknowledgments 239Abbreviations List 239References 24013 Chemometric Modeling of Algal and Daphnia Toxicity 243Luminita Crisan, Ana Borota, Alina Bora, Simona Funar-Timofei, and Gheorghe Ilia13.1 Introduction 24313.2 Algae Class 24713.3 Daphniidae Family 25613.4 Interspecies Correlation Estimation for Algal and Daphnia Aquatic Toxicity 26213.5 Conclusions 267Abbreviations List 268References 26814 Chemometric Modeling of Algal Toxicity 275Melek Türker Saçan, Serli Önlü, and Gulcin Tugcu14.1 Introduction 27514.2 Criteria Set for the Comparison of Selected QSAR Models 27714.3 Literature MLR Studies on Algae 28314.4 Conclusion 288References 28915 Chemometric Modeling of Daphnia Toxicity 293Amit Kumar Halder and Maria Natália Dias Soeiro Cordeiro15.1 Introduction 29315.2 QSTR and QSTTR Analyses 29415.3 QSTR/QSTT/QSTTR Modeling of Daphnia Toxicity 29515.4 Mechanistic Interpretations of Chemometric Models 30915.5 Conclusive Remarks and Future Directions 310Acknowledgment 311References 31116 Chemometric Modeling of Daphnia Toxicity: Quantum-Mechanical Insights 319Reenu and Vikas16.1 Introduction 31916.2 Quantum-Mechanical Methods 32116.3 Quantum-Mechanical Descriptors for Daphnia Toxicity 32316.4 Concluding Remarks and Future Outlook 325References 32617 Chemometric Modeling of Toxicity of Chemicals to Tadpoles 331Kabiruddin Khan and Kunal Roy17.1 Introduction 33117.2 Overview and Morphology of Tadpoles 33217.3 Reports of Tadpole Toxicity Due Various Environmental Contaminants: What Do We Know So Far? 34017.4 In silico Models Reported for Tadpole Ecotoxicity: A Literature Review 34117.5 Application of QSARs or Related Approaches in Modeling Tadpole Toxicity: A Future Perspective 35117.6 Conclusion 351Acknowledgment 351References 35218 Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria 359Kabiruddin Khan and Kunal Roy18.1 Introduction 35918.2 Marine Bacteria and Their Role in Nitrogen Fixing 36018.3 Marine Bacteria as Biomarkers for Ecotoxicity Estimation 36218.4 Chemometric Tools Applied in Ecotoxicity Evaluation of Marine Bacteria 36318.5 Conclusion 373Acknowledgment 373References 37419 Chemometric Modeling of Pesticide Aquatic Toxicity 377Alina Bora and Simona Funar-Timofei19.1 Introduction 37719.2 QSARs Models 38019.3 Conclusions 386Abbreviations List 386References 38720 Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State-of-the-Art 391Mabrouk Hamadache, Abdeltif Amrane, Othmane Benkortbi, and Salah Hanini20.1 Introduction 39120.2 Definition and Classification 39120.3 Advantage of Aquatic Plants 39220.4 Contaminants and Their Toxicity 39420.5 Chemometrics for Aquatic Plants Toxicity 40020.6 Review of Literature on Chemometrics for Aquatic Plants Toxicity 40020.7 Conclusions 406References 40721 Application of 3D-QSAR Approaches to Classification and Prediction of Aquatic Toxicity 417Sehan Lee and Mace G. Barron21.1 Introduction 41721.2 Principles of CAPLI 3D-QSAR 41921.3 Applications in Chemical Classification and Toxicity Prediction 42621.4 Limitation and Potential Improvement 42921.5 Conclusions and Recommendations 430Acknowledgments 430References 43022 QSAR Modeling of Aquatic Toxicity of Cationic Polymers 433Hans Sanderson, Pathan M. Khan, Supratik Kar, Kunal Roy, Anna M.B. Hansen, Kristin Connors, and Scott Belanger22.1 Introduction 43322.2 Materials and Methods 43422.3 Results and Discussion 44022.4 Conclusions 450Acknowledgments 450References 451Part IV Tools and Databases 45323 In Silico Platforms for Predictive Ecotoxicology: From Machine Learning to Deep Learning 455Yong Oh Lee and Baeckkyoung Sung23.1 Introduction 45523.2 Machine Learning and Deep Learning 45623.3 Toxicity Prediction Modeling 45823.4 Challenges and Future Directions 463References 46424 The Use and Evolution of Web Tools for Aquatic Toxicology Studies 473Renata P. B. Menezes, Natália F. Sousa, Luana de Morais e Silva, Luciana Scotti, Wilton Silva Lopes, and Marcus T. Scotti24.1 Introduction 47324.2 Methodologies Used in Aquatic Toxicology Tests 47424.3 Web Tools Used in Aquatic Toxicology 48224.4 Perspectives 487References 48825 The Tools for Aquatic Toxicology within the VEGAHUB System 493Emilio Benfenati, Anna Lombardo, Viktor Drgan, Marjana Novič, and Alberto Manganaro25.1 Introduction 49325.2 The VEGA Models 49525.3 ToxRead and Read-Across Within VEGAHUB 50525.4 Prometheus and JANUS 50625.5 The Future Developments 50825.6 Conclusions 509References 51026 Aquatic Toxicology Databases 513Supratik Kar and Jerzy Leszczynski26.1 Introduction 51326.2 Aquatic Toxicity 51426.3 Importance of Aquatic Toxicity Databases 51626.4 Characteristic of an Ideal Aquatic Toxicity Database 51626.5 Aquatic Toxicology Databases 51626.6 Overview and Conclusion 524Acknowledgments 524Conflicts of Interest 525References 52527 Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern: The LIFE-COMBASE Project 527María 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 Benfenati27.1 Introduction 52727.2 Database Compilation 53027.3 Development of the QSAR Models 53127.4 Prediction of Metabolites and their Associated Toxicity 53327.5 Implementation of the In Silico QSARs Within VEGA and Integration with Read Across Models in ToxRead 53427.6 Implementation of the LIFE-COMBASE Decision Support System 53727.7 Implementation of the LIFE-COMBASE Mobile App 54327.8 Concluding Remarks 543Acknowledgments 544References 54428 Image Analysis and Deep Learning Web Services for Nano informatics 547Anastasios 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 Melagraki27.1 Introduction 54727.2 NanoXtract 54927.3 DeepDaph 55627.4 Conclusions 560Acknowledgments 561References 561Index 565