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Chemometrics and Cheminformatics in Aquatic Toxicology [Hardback]

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  • Formāts: Hardback, 592 pages, height x width x depth: 10x10x10 mm, weight: 454 g
  • Izdošanas datums: 14-Jan-2022
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119681596
  • ISBN-13: 9781119681595
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Chemometrics and Cheminformatics in Aquatic ToxicologyPalielināt
  • Formāts: Hardback, 592 pages, height x width x depth: 10x10x10 mm, weight: 454 g
  • Izdošanas datums: 14-Jan-2022
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119681596
  • ISBN-13: 9781119681595
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781119681595.html
Keywords:
CHEMOMETRICS AND CHEMINFORMATICS IN AQUATIC TOXICOLOGY

Explore chemometric and cheminformatic techniques and tools in aquatic toxicology

Chemometrics and Cheminformatics in Aquatic Toxicology delivers an exploration of the existing and emerging problems of contamination of the aquatic environment through various metal and organic pollutants, including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, and more. The book discusses different chemometric and cheminformatic tools for non-experts and their application to the analysis and modeling of toxicity data of chemicals to various aquatic organisms.

You’ll learn about a variety of aquatic toxicity databases and chemometric software tools and webservers as well as practical examples of model development, including illustrations. You’ll also find case studies and literature reports to round out your understanding of the subject. Finally, you’ll learn about tools and protocols including machine learning, data mining, and QSAR and ligand-based chemical design methods.

Readers will also benefit from the inclusion of:

  • A thorough introduction to chemometric and cheminformatic tools and techniques, including machine learning and data mining
  • An exploration of aquatic toxicity databases, chemometric software tools, and webservers
  • Practical examples and case studies to highlight and illustrate the concepts contained within the book
  • A concise treatment of chemometric and cheminformatic tools and their application to the analysis and modeling of toxicity data

Perfect for researchers and students in chemistry and the environmental and pharmaceutical sciences, Chemometrics and Cheminformatics in Aquatic Toxicology will also earn a place in the libraries of professionals in the chemical industry and regulators whose work involves chemometrics.

Preface xxi
Part I Introduction 1(68)
1 Water Quality and Contaminants of Emerging Concern (CECs)
3(20)
Antonio Juan Garcia-Fernandez
Silvia Espin
Pilar Gomez-Ramirez
Pablo Sanchez-Virosta
Isabel Navas
1.1 Introduction: Water Quality and Emerging Contaminants
3(3)
1.2 Contaminants of Emerging Concern
6(8)
1.2.1 Pharmaceuticals
6(2)
1.2.2 Personal Care Products
8(1)
1.2.3 Nanomaterials
9(1)
1.2.4 Plasticizers
9(1)
1.2.5 Surfactants and Metabolites
10(1)
1.2.6 Flame Retardants
10(1)
1.2.7 Industrial Additives and Agents
11(1)
1.2.8 Anticorrosives and Antifouling Agents
12(1)
1.2.9 Natural Emerging Contaminants: Mycotoxins and Phytotoxins
13(1)
1.3 Summary and Recommendations for Future Research
14(1)
References
14(9)
2 The Effects of Contaminants of Emerging Concern on Water Quality
23(22)
Heiko L. Schoenfuss
2.1 Introduction
23(4)
2.1.1 Sources of CECs to the Aquatic Ecosystem
23(1)
2.1.2 Fate of CECs in Aquatic Environments
24(3)
2.2 Assessing the Effects of CECs in Aquatic Life
27(7)
2.2.1 Pharmaceuticals
28(3)
2.2.2 Personal Care Products
31(1)
2.2.3 Agricultural Pesticides
31(1)
2.2.4 Industrial Chemicals
32(2)
2.3 Multiple Stressors
34(1)
2.3.1 Mixtures of CECs
34(1)
2.3.2 Interactions of CECs and Other Environmental Stressors
34(1)
2.3.3 Climate Change
34(1)
2.4 Conclusions
35(1)
Acknowledgments
35(1)
References
35(10)
3 Chemometrics: Multivariate Statistical Analysis of Analytical Chemical and Biomolecular Data
45(16)
Richard G. Brereton
3.1 Introduction
45(1)
3.2 Historic Origins
45(1)
3.3 Applied Statistics
46(2)
3.4 Analytical and Physical Chemistry
48(1)
3.5 Scientific Computing
49(1)
3.6 Development from the 1980s
50(2)
3.7 A Review of the Main Methods
52(1)
3.8 Experimental Design
52(1)
3.9 Principal Components Analysis and Pattern Recognition
53(1)
3.10 Multivariate Signal Analysis
54(1)
3.11 Multivariate Calibration
55(1)
3.12 Digital Signal Processing and Time Series Analysis
56(1)
3.13 Multiway Methods
56(1)
3.14 Conclusion
56(1)
References
57(4)
4 An Introduction to Chemometrics and Cheminformatics
61(8)
Chanin Nantasenamat
4.1 Brief History of Chemometrics/Cheminformatics
61(1)
4.2 Current State of Cheminformatics
62(1)
4.3 Common Cheminformatics Tasks
62(1)
4.4 Cheminformatics Toolbox
63(2)
4.5 Conclusion
65(1)
References
65(4)
Part II Chemometric and Cheminformatic Tools and Protocols 69(132)
5 An Introduction to Some Basic Chemometric Tools
71(18)
Lennart Eriksson
Erik Johansson
Johan Trygg
5.1 Introduction
71(1)
5.2 Example Datasets
72(1)
5.2.1 Example 1 - The Mono-Substituted Nitrobenzenes Dataset
72(1)
5.2.2 Example 2 - The Oil Offshore Production Emission Dataset
72(1)
5.3 Data Analytical Methods
73(5)
5.3.1 Pretreatment Methods
73(1)
5.3.2 Principal Components Analysis (PCA)
74(1)
5.3.3 Partial Least Squares Projections to Latent Structures (PLS)
75(2)
5.3.4 Orthogonal Partial Least Squares (OPLS®)
77(1)
5.3.5 Cross-Validation
77(1)
5.4 Results
78(7)
5.4.1 Results for Example 1
78(1)
5.4.1.1 PCA Modeling
78(1)
5.4.1.2 Single-Y QSAR Modeling
78(1)
5.4.1.3 Multi-Y QSAR Modeling
80(3)
5.4.2 Results for Example 2
83(1)
5.4.2.1 Initial Multi-Y PLS Model
83(1)
5.4.2.2 Updated Multi-Y PLS Model
84(1)
5.5 Discussion
85(2)
References
87(2)
6 From Data to Models: Mining Experimental Values with Machine Learning Tools
89(36)
Giuseppina Gini
Emilio Benfenati
6.1 Introduction
89(2)
6.2 Data and Models
91(3)
6.2.1 Data
91(1)
6.2.2 Models
92(2)
6.3 Basic Methods in Model Development with ML
94(9)
6.3.1 Inputs to the Model
94(1)
6.3.2 Output of the Model
95(1)
6.3.3 Basic Algorithms
96(1)
6.3.3.1 Inferring Simple Classification Rules
96(1)
6.3.3.2 Naive Bayes
96(1)
6.3.3.3 Constructing Decision Trees
97(1)
6.3.3.4 Covering Algorithms
97(1)
6.3.3.5 Association Rules
98(1)
6.3.3.6 Linear Models
98(1)
6.3.3.7 Instance-Based Learning and Similarity Search
99(1)
6.3.3.8 Clustering
99(1)
6.3.4 Evaluating What the Model Has Learned from Data
99(1)
6.3.4.1 Training, Validation, and Testing Sets
100(1)
6.3.4.2 Cross Validation
100(1)
6.3.4.3 Performance for Classifiers
100(1)
6.3.4.4 Performance for Numerical Predictions
102(1)
6.3.5 Model Interpretability
102(1)
6.4 More Advanced ML Methodologies
103(10)
6.4.1 Classifiers: from Decision Trees to Ensemble
103(1)
6.4.1.1 Random Forest
104(1)
6.4.1.2 Learning the Integration and Stacking
105(1)
6.4.2 Mining Datasets to Extract Frequent Subgroups
106(1)
6.4.3 Kernel Methods and Support Vector Machine (SVM)
107(2)
6.4.4 From Perceptron to Neural Nets
109(1)
6.4.4.1 Network Terminology
110(1)
6.4.4.2 Training the Net
110(1)
6.4.4.3 Hyperparameters and Optimization
111(1)
6.4.4.4 Use of Trained Networks
112(1)
6.4.4.5 Neural Networks in QSAR
113(1)
6.5 Deep Learning
113(7)
6.5.1 Main DNN Architectures
113(1)
6.5.1.1 Convolutional Neural Network (CNN)
114(1)
6.5.1.2 Recurrent Neural Network (RNN)
114(1)
6.5.2 Interpretation of DNN Models
115(1)
6.5.3 Consequences of Deep Learning for QSAR
116(4)
6.6 Conclusions
120(1)
References
121(4)
7 Machine Learning Approaches in Computational Toxicology Studies
125(32)
Pravin Ambure
Stephen J. Barigye
Rafael Gozalbes
7.1 Introduction
125(2)
7.1.1 Computer-Based Toxicity Prediction
125(1)
7.1.2 Brief History of QSAR and Modern Machine Learning Techniques
125(2)
7.2 Toxicity Data Set Preparation
127(1)
7.2.1 Data Collection and Chemical Structure Representation
127(1)
7.2.2 Descriptors and Fingerprints
127(1)
7.3 Machine-Learning Techniques
128(17)
7.3.1 Unsupervised Learning
128(1)
7.3.1.1 k-Means Clustering
128(1)
7.3.1.2 Hierarchical Clustering
130(1)
7.3.1.3 Principal Component Analysis
131(1)
7.3.2 Supervised Learning
132(1)
7.3.2.1 Linear Regression Analysis
133(1)
7.3.2.2 Logistic Regression
134(1)
7.3.2.3 Linear Discriminant Analysis (LDA)
135(1)
7.3.2.4 k-Nearest Neighbor
135(1)
7.3.2.5 Naive Bayes
136(1)
7.3.2.6 Decision Trees
139(1)
7.3.2.7 Random Forest
140(1)
7.3.2.8 Support Vector Machine
141(1)
7.3.2.9 Artificial Neural Network and Deep Learning
142(2)
7.3.3 Semi-Supervised Learning
144(1)
7.4 Model Evaluation
145(1)
7.5 Freely Available Software Tools and Open-Source Libraries Relevant to Machine Learning
146(2)
7.6 Concluding Remarks
148(1)
Acknowledgment
148(1)
References
148(9)
8 Counter-Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity
157(10)
Viktor Drgan
Marjan Vracko
8.1 Introduction
157(1)
8.2 Examples of Counter-Propagation Artificial Neural Networks in Fish Toxicity Modeling
158(5)
8.3 Counter-Propagation Artificial Neural Networks
163(1)
8.4 Conclusions
164(1)
References
164(3)
9 Aiming High versus Aiming All: Aquatic Toxicology and QSAR Multitarget Models
167(14)
Ana S. Moura
M. Natalia
D.S. Cordeiro
9.1 Introduction
167(1)
9.2 Multitarget QSARS and Aquatic Toxicology
168(7)
9.2.1 Multitarget QSARS: Basics Overview
168(1)
9.2.1.1 Descriptor Development
168(1)
9.2.1.2 Perturbation Theory
169(1)
9.2.1.3 Machine Learning
170(1)
9.2.2 Mt-QSAR and the Biotarget Perspective: A Review from Selected Works
170(1)
9.2.2.1 Methodological Identity
171(1)
9.2.2.2 Selected Works: Analysis per Bio Target
174(1)
9.3 Biotargets and Aqueous Environmental Assessment: Solutions and Recommendations
175(1)
9.4 Future Perspectives and Conclusion
175(1)
References
176(5)
10 Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity
181(20)
S. Raimondo
C.M. Lavelle
M.G. Barron
10.1 Introduction
181(2)
10.2 Acute Toxicity Estimation
183(3)
10.2.1 Quantitative Structure-Activity Relationship (QSAR) Models
184(1)
10.2.2 Interspecies Correlation Estimation (ICE) Models
184(1)
10.2.3 Species Sensitivity Distributions (SSDs)
185(1)
10.2.4 Linking Acute Toxicity Models
185(1)
10.3 Sublethal Toxicity Extrapolation
186(5)
10.3.1 Genomics and Sequence-Based Relationships
187(1)
10.3.2 Chemical Proteomics
188(1)
10.3.3 Differential Expression and Pathway Analysis
189(2)
10.4 Discussion
191(1)
10.5 Conclusions
192(1)
Disclaimer
192(1)
References
193(8)
Part III Case Studies and Literature Reports 201(252)
11 The QSAR Paradigm to Explore and Predict Aquatic Toxicity
203(24)
Fotios Tsopelas
Anna Tsantili-Kakoulidou
11.1 Introduction
203(1)
11.2 Application of QSAR Methodology to Predict Aquatic Toxicity
204(5)
11.2.1 Overview
204(1)
11.2.2 Aquatic Toxicity Endpoints and Relevant Databases
205(1)
11.2.3 Criteria for Robust QSAR Models
206(1)
11.2.4 MOA-Based Aquatic Toxicity QSAR (QSTR)
206(1)
11.2.5 Software Tools for Ecotoxicological Endpoints
207(2)
11.3 QSAR for Narcosis - The Impact of Hydrophobicity
209(4)
11.3.1 Linear Solvation Energy Relationships for Narcosis
211(1)
11.3.2 Application of Chromatographic Systems for Building Narcotic Models
212(1)
11.4 Excess Toxicity - Overview
213(3)
11.4.1 QSAR (QSTR) Models for Reactive and Specific Acting Chemicals
213(3)
11.5 Predictions of Bioconcentration Factor
216(2)
11.6 Conclusions
218(1)
References
219(8)
12 Application of Cheminformatics to Model Fish Toxicity
227(16)
Sorin Avram
Simona Funar-Timofei
Gheorghe Ilia
12.1 Introduction
227(1)
12.2 Fish Toxicities
228(1)
12.3 Toxicity in Fish Families and Species
229(2)
12.4 The Fathead Minnow, the Rainbow Trout, and the Bluegill
231(1)
12.5 Toxicity Variations in FIT Compounds
232(1)
12.6 Modeling Wide-Range Toxicity Compounds
233(3)
12.7 Further Evaluations
236(1)
12.8 Alternative Approaches
237(1)
12.9 Mechanisms of Action
238(1)
12.10 Conclusions
239(1)
Acknowledgments
239(1)
Abbreviations List
239(1)
References
240(3)
13 Chemometric Modeling of Algal and Daphnia Toxicity
243(32)
Luminita Crisan
Ana Borota
Alina Bora
Simona Funar-Timofei
Gheorghe Ilia
13.1 Introduction
243(4)
13.2 Algae Class
247(9)
13.2.1 Short Characterization of Algae Class
247(1)
13.2.2 QSAR Models Developed Using the Algae
248(8)
13.3 Daphniidae Family
256(6)
13.3.1 Short Characterization of Daphniidae Family
256(1)
13.3.2 QSAR Models Developed Using Daphnia magna
257(5)
13.4 Interspecies Correlation Estimation for Algal and Daphnia Aquatic Toxicity
262(5)
13.4.1 Algal and Daphnia Toxicity Correlation
262(1)
13.4.2 Algal, Daphnia and Other Species Toxicity Correlation
262(4)
13.4.3 Daphnia and Other Species Toxicity Correlation
266(1)
13.4.4 Algae Species Toxicity Correlations
267(1)
13.4.5 Algal and Other Species Toxicity Correlation
267(1)
13.5 Conclusions
267(1)
Abbreviations List
268(1)
References
268(7)
14 Chemometric Modeling of Algal Toxicity
275(18)
Melek Turker Sacan
Serli Onlu
Gulcin Tugcu
14.1 Introduction
275(2)
14.1.1 Environmental Importance of Algae
275(1)
14.1.2 OECD Principles
276(1)
14.1.3 Brief Summary of Algal QSAR Models
276(1)
14.2 Criteria Set for the Comparison of Selected QSAR Models
277(6)
14.2.1 The Modeled Endpoints
277(1)
14.2.2 Descriptors
277(4)
14.2.3 Model Performance
281(1)
14.2.4 Applicability Domain
282(1)
14.2.5 Software Used for QSAR Modeling
283(1)
14.3 Literature MLR Studies on Algae
283(5)
14.4 Conclusion
288(1)
References
289(4)
15 Chemometric Modeling of Daphnia Toxicity
293(26)
Amit Kumar Haider
Maria Natalia Dias Soeiro Cordeiro
15.1 Introduction
293(1)
15.2 QSTR and QSTTR Analyses
294(1)
15.3 QSTR/QSTT/QSTTR Modeling of Daphnia Toxicity
295(14)
15.3.1 Categorized Chemicals
295(1)
15.3.1.1 Ionic Liquids (ILs)
295(1)
15.3.1.2 Nanomaterials
297(1)
15.3.1.3 Surfactants
298(1)
15.3.1.4 Biocides and Agrochemicals
299(1)
15.3.1.5 Pharmaceuticals and Cosmetics
301(1)
15.3.1.6 Compounds with Specific Chemical Groups
303(2)
15.3.2 Non-categorized Chemicals
305(1)
15.3.2.1 QSTR Models
305(4)
15.4 Mechanistic Interpretations of Chemometric Models
309(1)
15.5 Conclusive Remarks and Future Directions
310(1)
Acknowledgment
311(1)
References
311(8)
16 Chemometric Modeling of Daphnia Toxicity: Quantum-Mechanical Insights
319(12)
Reenu
Vikos
16.1 Introduction
319(2)
16.2 Quantum-Mechanical Methods
321(2)
16.3 Quantum-Mechanical Descriptors for Daphnia Toxicity
323(2)
16.4 Concluding Remarks and Future Outlook
325(1)
References
326(5)
17 Chemometric Modeling of Toxicity of Chemicals to Tadpoles
331(28)
Kabiruddin Khan
Kunal Roy
17.1 Introduction
331(1)
17.2 Overview and Morphology of Tadpoles
332(8)
17.2.1 Tadpole as a Target for Ecotoxicity Testing
333(1)
17.2.1.1 Why Tadpole's Toxicity Matters?
333(1)
17.2.1.2 Tadpole Species used for Ecotoxicity Studies
333(1)
17.2.1.3 Toxicity Endpoint(s) Studied on Tadpoles
333(1)
17.2.1.4 Observable Response(s) Measured on Tadpoles for Toxicity Assessment
333(1)
17.2.1.5 Response Sites on the Tadpoles
340(1)
17.3 Reports of Tadpole Toxicity Due Various Environmental Contaminants: What Do We Know So Far?
340(1)
17.4 In silico Models Reported for Tadpole Ecotoxicity: A Literature Review
341(10)
17.5 Application of QSARs or Related Approaches in Modeling Tadpole Toxicity: A Future Perspective
351(1)
17.6 Conclusion
351(1)
Acknowledgment
351(1)
References
352(7)
18 Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria
359(18)
Kabiruddin Khan
Kunal Roy
18.1 Introduction
359(1)
18.1.1 Marine Bacteria: A Source of Ocean's Wealth
359(1)
18.1.2 Morphology of Marine Bacteria
359(1)
18.1.3 Marine Bacteria in Symbiotic Association with Other Species
360(1)
18.1.4 Marine Bacteria as Nitrogen Fixers
360(1)
18.2 Marine Bacteria and Their Role in Nitrogen Fixing
360(2)
18.2.1 Marine Bacteria That Actually "Fix" Nitrogen
360(2)
18.2.2 Marine Bacteria Which Are Involved in Nitrification
362(1)
18.2.3 Primary Producers Marine Bacteria Those Who Do Not Fix Nitrogen
362(1)
18.3 Marine Bacteria as Biomarkers for Ecotoxicity Estimation
362(1)
18.4 Chemometric Tools Applied in Ecotoxicity Evaluation of Marine Bacteria
363(10)
18.4.1 Ecotoxicity Evaluations of Organic Compounds
363(1)
18.4.1.1 Ecotoxicity Evaluations of Dithiocarbamates (DCs) and Their Derivatives
363(1)
18.4.1.2 Ecotoxicity Evaluations of Aliphatic Organic Compounds
365(1)
18.4.1.3 Ecotoxicity Evaluations of Polycyclic Aromatic Hydrocarbons (PAHs)
365(1)
18.4.1.4 Ecotoxicity Evaluations of Organic Phenols with Propyl and Butyl Substitutions
366(1)
18.4.1.5 Ecotoxicity Evaluations of Organic Mixture Toxicity
366(1)
18.4.1.6 Ecotoxicity Evaluations of Organic Chemicals Using Interspecies Modeling
367(1)
18.4.1.7 Ecotoxicity Evaluations of Antimicrobial Organic Chemicals
368(1)
18.4.1.8 Ecotoxicity Evaluations of Organic Chemicals Using Baseline Toxicity
368(1)
18.4.1.9 Ecotoxicity Evaluations of Biofouling Agents (Organic Chemicals)
368(1)
18.4.1.10 Ecotoxicity Evaluations of Organic Chemicals with Nonlinear Modeling
369(1)
18.4.2 Ecotoxicity Evaluations Using Capacity Factors (k')
369(1)
18.4.3 Ecotoxicity Evaluations of Shale Oil Components
370(1)
18.4.4 Ecotoxicity Evaluations of Human Pharmaceuticals
371(1)
18.4.5 Ecotoxicity Evaluations of Ionic Liquids (ILs)
371(1)
18.4.5.1 Study 1
371(1)
18.4.5.2 Study 2
372(1)
18.4.5.3 Study 3
373(1)
18.5 Conclusion
373(1)
Acknowledgment
373(1)
References
374(3)
19 Chemometric Modeling of Pesticide Aquatic Toxicity
377(14)
Alina Bora
Simona Funar-Timofei
19.1 Introduction
377(3)
19.2 QSARs Models
380(6)
19.2.1 QSAR Models Developed Using Fish Species
380(1)
19.2.2 QSAR Models Developed Using Zebrafish Embryos
381(1)
19.2.3 QSAR Models Developed Using Algae Species
381(1)
19.2.4 QSAR Models Developed Using Americamysis Bahia Species
382(1)
19.2.5 QSAR Models Developed Using Daphnia magna
383(1)
19.2.6 QSAR/QAAR Models Developed Using Interspecies Correlations
384(2)
19.3 Conclusions
386(1)
Abbreviations List
386(1)
References
387(4)
20 Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State-of-the-Art
391(26)
Mabrouk Hamadache
Abdeltif Amrane
Othmane Benkortbi
Salah Hanini
20.1 Introduction
391(1)
20.2 Definition and Classification
391(1)
20.3 Advantage of Aquatic Plants
392(2)
20.3.1 Ecosystems Benefits
392(1)
20.3.2 Economic Benefits
393(1)
20.3.3 Phytoremediation Using Aquatic Plants
393(1)
20.4 Contaminants and Their Toxicity
394(6)
20.5 Chemometrics for Aquatic Plants Toxicity
400(1)
20.6 Review of Literature on Chemometrics for Aquatic Plants Toxicity
400(6)
20.6.1 Toxicity of Pharmaceuticals
400(1)
20.6.2 Toxicity of Pesticides
401(1)
20.6.3 Toxicity of Nanoparticles
402(1)
20.6.4 Toxicity of Heavy Metal and Metalloids
403(2)
20.6.5 Toxicity of Others Pollutants
405(1)
20.7 Conclusions
406(1)
References
407(10)
21 Application of 3D-QSAR Approaches to Classification and Prediction of Aquatic Toxicity
417(16)
Sehan Lee
Mace G. Barron
21.1 Introduction
417(2)
21.1.1 Environmental Risk Assessment of Chemicals
417(1)
21.1.2 In silico Models in Environmental Risk Assessment
417(1)
21.1.3 Introduction and Limitation of the Previous QSAR Approaches
418(1)
21.1.4 Challenges and Improvement Through 3D-QSAR
418(1)
21.2 Principles of CAPLI 3D-QSAR
419(7)
21.2.1 Docking Protocols
420(1)
21.2.2 Data Preparation
421(1)
21.2.3 Structure-based Pharmacophore and 3D-fingerprint Descriptors
422(3)
21.2.4 CAPLI 3D-QSAR Development and Validation
425(1)
21.2.5 Prediction of Binding Mode and Affinity
426(1)
21.3 Applications in Chemical Classification and Toxicity Prediction
426(3)
21.3.1 Mechanism-Based Classification of OP Inhibitors
426(2)
21.3.2 Species Susceptibility Prediction
428(1)
21.3.3 Structure-Toxicity Relationship Analysis
429(1)
21.4 Limitation and Potential Improvement
429(1)
21.4.1 Convolutional Neural Network
429(1)
21.5 Conclusions and Recommendations
430(1)
Acknowledgments
430(1)
References
430(3)
22 QSAR Modeling of Aquatic Toxicity of Cationic Polymers
433(20)
Hans Sanderson
Pathan M. Khan
Supratik Kar
Kunal Roy
Anna M.B. Hansen
Kristin Connors
Scott Belanger
22.1 Introduction
433(1)
22.2 Materials and Methods
434(6)
22.2.1 Polymers
434(1)
22.2.2 Dataset
434(1)
22.2.3 Descriptor Calculation
435(4)
22.2.4 Dataset Division
439(1)
22.2.5 Model Development
439(1)
22.2.6 Model Validation
440(1)
22.3 Results and Discussion
440(10)
22.3.1 QSTR Modeling for Fish Toxicity 96 h Dataset
440(3)
22.3.2 QSTR Modeling for Daphnia magna Toxicity 48 h Dataset
443(2)
22.3.3 QSTR Modeling for Green Algae Toxicity 96 h Dataset
445(1)
22.3.4 QSTR Modeling for Chronic Toxicity Against Green Algae
445(1)
22.3.5 Interspecies Modeling of Polymers
446(1)
22.3.5.1 i-QSTR Modeling Between D. magna (48h) and Fish (96h)
446(1)
22.3.5.2 i-QSTR Modeling Between Fish (96 h) and D. magna (48 h) Toxicities
447(1)
22.3.5.3 i-QSTR Modeling Between Acute Green Algae (96h) and Acute Fish (96h) Toxicities
448(1)
22.3.5.4 i-QSTR Modeling Between Fish (96 h) and Acute Green Algae (96 h) Toxicities
448(1)
22.3.5.5 i-QSTR Modeling Between D. magna (48 h) and Acute Green Algae (96 h) Toxicities
448(1)
22.3.5.6 i-QSTR Modeling Between Acute Green algae (96 h) and D. magna (48 h) Toxicities
449(1)
22.4 Conclusions
450(1)
Acknowledgments
450(1)
References
451(2)
Part IV Tools and Databases 453(112)
23 In Silico Platforms for Predictive Ecotoxicotogy: From Machine Learning to Deep Learning
455(18)
Yong Oh Lee
Baeckkyoung Sung
23.1 Introduction
455(1)
23.2 Machine Learning and Deep Learning
456(2)
23.2.1 Support Vector Machines
456(1)
23.2.2 Random Forest
457(1)
23.2.3 Deep Neural Networks
457(1)
23.3 Toxicity Prediction Modeling
458(5)
23.3.1 General Procedure of Modeling
458(1)
23.3.2 QSAR
458(1)
23.3.2.1 Machine Learning and Deep Learning in QSAR Modeling
460(1)
23.3.2.2 Useful Tools for QSAR Modeling
460(1)
23.3.3 Molecular Docking
461(1)
23.3.4 Read-Across
462(1)
23.3.5 Structural Alerts
462(1)
23.3.6 Adverse Outcome Pathway
463(1)
23.4 Challenges and Future Directions
463(1)
References
464(9)
24 The Use and Evolution of Web Tools for Aquatic Toxicology Studies
473(20)
Renato P.B. Menezes
Natalia F. Sousa
Luana de Morals e Silva
Luciana Scotti
Wilton Silva Lopes
Marcus T. Scotti
24.1 Introduction
473(1)
24.2 Methodologies Used in Aquatic Toxicology Tests
474(8)
24.2.1 Database
474(1)
24.2.1.1 Ambit Database
474(1)
24.2.1.2 Online Chemical Database (OCHEM)
474(1)
24.2.1.3 European Chemical Agency (ECHA)
474(1)
24.2.1.4 Registration, Evaluation, Authorization, Evaluation, and Restriction of Chemical (REACH)
474(1)
24.2.1.5 The Organization for Economic Cooperation and Development (OECD) Guidelines
474(1)
24.2.1.6 Computer-Assisted Evaluation of Industrial Chemical Substances According to Regulations (CAESAR)
479(1)
24.2.1.7 Catalogic
480(1)
24.2.2 Toxicity
481(1)
24.2.3 Quantitative Structure-Activity Relationships (QSARs) Between Chemical Structures and Biological Activity in Aquatic Toxicity Studies
481(1)
24.3 Web Tools Used in Aquatic Toxicology
482(5)
24.3.1 Aggregated Computational Toxicology Online Resource (ACToR)
482(1)
24.3.2 ECOTOXicology (ECOTOX)
482(1)
24.3.3 OASIS
482(1)
24.3.4 TOXMATCH
483(1)
24.3.5 OSIRIS
483(1)
24.3.6 BIOWIN Models
484(1)
24.3.7 AdmetSar
484(1)
24.3.8 Chembench
484(1)
24.3.9 Ecological Structure-Activity Relationships (ECOSAR)
485(1)
24.3.10 OECD QSAR Toolbox
485(1)
24.3.11 PASS
486(1)
24.3.12 Applications of in silico Techniques to Aquatic Toxicology Tests
486(1)
24.4 Perspectives
487(1)
References
488(5)
25 The Tools for Aquatic Toxicology within the VEGAHUB System
493(20)
Emilio Benfenati
Anna Lombardo
Viktor Drgan
Marjana Novic
Alberto Manganaro
25.1 Introduction
493(2)
25.2 The VEGA Models
495(10)
25.2.1 The VEGA Models for Aquatic Toxicity
495(1)
25.2.2 The Example of the Fish Acute Toxicity Model Developed Using Neural Networks
495(4)
25.2.3 The Differences Between the Aquatic Toxicity Models
499(1)
25.2.4 The Components of the Applicability Domain Index
499(2)
25.2.5 The Evaluation of the Results of the VEGA Models
501(1)
25.2.5.1 The Evaluation of the Results of the Single Model
502(1)
25.2.5.2 The Evaluation of the Results of the Multiple Models for the Same Endpoint
504(1)
25.3 ToxRead and Read-Across Within VEGAHUB
505(1)
25.4 Prometheus and JANUS
506(2)
25.5 The Future Developments
508(1)
25.5.1 The VERMEER Project
508(1)
25.5.2 The toDIVINE Project
509(1)
25.6 Conclusions
509(1)
References
510(3)
26 Aquatic Toxicology Databases
513(14)
Supratik Kar
Jerzy Leszczynski
26.1 Introduction
513(1)
26.2 Aquatic Toxicity
514(2)
26.2.1 Aquatic Toxicity Test
514(1)
26.2.2 Aquatic Test Species
514(2)
26.3 Importance of Aquatic Toxicity Databases
516(1)
26.4 Characteristic of an Ideal Aquatic Toxicity Database
516(1)
26.5 Aquatic Toxicology Databases
516(8)
26.5.1 Acute Toxicity Database
516(2)
26.5.2 Aquatic Toxicity Information Retrieval (AQUIRE)
518(1)
26.5.3 Ecotoxicology Database (ECOTOX)
519(2)
26.5.4 Environmental Residue Effects Database (FRED)
521(1)
26.5.5 EnviroTox
521(1)
26.5.6 MOAtox
522(2)
26.5.7 Toxicity/Residue Database
524(1)
26.6 Overview and Conclusion
524(1)
Acknowledgments
524(1)
Conflicts of Interest
525(1)
References
525(2)
27 Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern: The LIFE-COMBASE Project
527(20)
Maria Blazquez
Oscar Andreu-Sanchez
Arantxa Ballesteros
Maria Luisa Fernandez-Cruz
Carlos Fito
Sergi Gomez-Ganau
Rafael Gozalaes
David Hernandez-Moreno
Jesus Vicente de Julian-Ortiz
Anna Lombardo
Marco Marzo
Irati Ranero
Nuria Ruiz-Costa
Jose Vicente Tarazona-Diez
Emilio Benfenati
27.1 Introduction
527(3)
27.1.1 Biocides Regulation
527(1)
27.1.2 Alternative Methods
528(1)
27.1.3 Computational Approaches on Biocides: State of the Art
529(1)
27.1.4 The LIFE-COMBASE Project
529(1)
27.2 Database Compilation
530(1)
27.2.1 Criteria Definition for the Selection of Biocidal Active Substances
530(1)
27.2.2 Sources of Data
531(1)
27.3 Development of the QSAR Models
531(2)
27.3.1 Preparation of the Data Sets
531(1)
27.3.2 QSAR Models for Microorganisms
532(1)
27.3.3 QSAR Models for Algae
532(1)
27.3.4 QSAR Models for Daphnia magna
533(1)
27.3.5 QSAR Models on Fish
533(1)
27.4 Prediction of Metabolites and their Associated Toxicity
533(1)
27.5 Implementation of the In Silico QSARs Within VEGA and Integration with Read Across Models in ToxRead
534(3)
27.5.1 Implementation of the QSAR Models Within VEGA
534(3)
27.5.2 Implementation of the Rules for Read-Across and Grouping Within ToxRead
537(1)
27.5.3 Integration of QSARs and Read-Across Within a Weight-of-evidence Strategy
537(1)
27.6 Implementation of the LIFE-COMBASE Decision Support System
537(6)
27.6.1 Database Search Engine
538(2)
27.6.2 Biocides' Chemical Space
540(1)
27.6.3 Metabolites Prediction
540(1)
27.6.4 Calculation of Aquatic Ecotoxicity
540(1)
27.6.5 Generation of Alternative Biocide Structures
541(2)
27.7 Implementation of the LIFE-COMBASE Mobile App
543(1)
27.8 Concluding Remarks
543(1)
Acknowledgments
544(1)
References
544(3)
28 Image Analysis and Deep Learning Web Services for Nano informatics
547(18)
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
Georgia Melagraki
28.1 Introduction
547(2)
28.2 NanoXtract
549(7)
28.2.1 NanoXtract Environment and Image Uploading
550(1)
28.2.2 Computational Workflow and Available Settings
550(4)
28.2.3 Produced Results
554(2)
28.3 DeepDaph
556(4)
28.3.1 DeepDaph Environment
558(2)
28.3.2 Produced Results
560(1)
28.4 Conclusions
560(1)
Acknowledgments
561(1)
References
561(4)
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.