Ecotoxicological QSARs (Methods in Pharmacology and Toxicology)

Dedication
Preface
Contents
Contributors
About the Editor
Part I: Introduction
	Chapter 1: Ecotoxicological Risk Assessment in the Context of Different EU Regulations
		1 Introduction to the European Union
			1.1 European Union Law
			1.2 Legislative and Nonlegislative Acts in the EU
			1.3 EU Policies on Environment
		2 EU Laws Related to Ecotoxicology Risk Assessment
			2.1 REACH Regulation (EC) 1907/2006
			2.2 Nanoforms in Amended REACH Regulation (EU) 2018/1881
			2.3 Biocidal Products Regulation (EU) 528/2012
			2.4 Plant Protection Products Regulation (EC) 1107/2009
			2.5 Pharmaceuticals in the Environment: EU Strategic Approach
				2.5.1 Environmental Risk Assessment of Medicinal Products
			2.6 Classification, Labeling, and Packaging Regulation (EC) 1272/2008
			2.7 EU Water Framework Directive 2000/60/EC
			2.8 Crosscutting Legislation in Ecotoxicology Tests
		3 European Strategy for a Nontoxic Environment
		4 Overview
		References
	Chapter 2: A Brief Introduction to Quantitative Structure-Activity Relationships as Useful Tools in Predictive Ecotoxicology
		1 Introduction
		2 Definition and Constituents of QSAR
			2.1 What Is QSAR?
				2.1.1 Biological Data
				2.1.2 Chemical Data/Descriptors
				2.1.3 Molecular Descriptors: Different Types
				2.1.4 Division of the Dataset
				2.1.5 Feature Selection
				2.1.6 Modeling Algorithms and Chemometric Tools Used in QSAR
				2.1.7 Checking Domain of Applicability of Developed Models
				2.1.8 Validation of QSAR Models
				2.1.9 Mechanistic Interpretation of the QSAR Model
				2.1.10 A Few Important Issues in QSAR
			2.2 Ecotoxicity Predictions
		3 Why QSAR in Ecotoxicity Predictions: Can It Really Reduce Animal Experimentation?
		4 Ecotoxicological Data Sources, Expert Systems, and Freely Available QSAR Tools
		5 Applications of QSAR in Ecotoxicological QSAR Studies
			5.1 QSAR Models for Toxicity of Ionic Liquids
			5.2 QSAR Models for Nanomaterial Toxicity (Nano-QSAR)
			5.3 QSAR Models for Toxicity of Contaminants of Emerging Concern (CECs)
		6 Read-Across (RA) as a Tool to Predict Missing Ecotoxicological Data
			6.1 Application of Read-Across in Ecotoxicological Data Gap Filling
		7 Overview and Conclusion
		References
	Chapter 3: Best Practices for Constructing Reproducible QSAR Models
		1 Quantitative Structure-Activity Relationship
		2 Laboratory Notebooks: Past and Present
		3 Data Sharing
		4 Data, Chemical Structure, Conformation, and Descriptors
		5 QSAR Model Building Process
		6 Interactive Notebooks
		7 Tutorials on Using Jupyter Notebook for QSAR Modeling
			7.1 Tutorial 1: Installing Miniconda
			7.2 Tutorial 2: Installing Packages in Conda
				7.2.1 Installing Python Packages
				7.2.2 Installing Multiple Python Packages at Once
				7.2.3 Installing Python Packages from Channels
			7.3 Tutorial 3: Running the Jupyter Notebook
		8 Conclusion
		References
	Chapter 4: Wildlife Sentinels for Human and Environmental Health Hazards in Ecotoxicological Risk Assessment
		1 Animals Can Reflect Human and Environmental Health Risk: Background
		2 Biomonitoring Studies in Wildlife
			2.1 Endocrine Disruption: Reproductive and Development Effects
			2.2 Research Challenges on EDCs in Human and Wildlife
			2.3 Carcinogenic Effects
			2.4 Behavioral and Neurotoxic Effects
			2.5 Immune Effects
			2.6 Other Chronic Effects
		3 Overview
		References
Part II: Methods and Protocols
	Chapter 5: Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Datasets
		1 Introduction
		2 Importance of Data Curation in QSAR Studies
		3 Key Steps Involved in Data Curation for QSAR/QSTR/QSPR Studies
		4 Recent Advances in Data Curation Tools
		5 Prospects
		References
	Chapter 6: Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling
		1 Introduction
		2 Machine Learning in QSAR for Environmental Protection
			2.1 CMR Assessment
				2.1.1 ML Methods in QSAR for Cancerogenicity
				2.1.2 ML Methods in QSAR for Mutagenicity
				2.1.3 ML Methods in QSAR for Reproductive Toxicity
			2.2 PBT Assessment
				2.2.1 ML Methods in QSAR for Persistence
				2.2.2 ML Methods in QSAR for Bioaccumulation
				2.2.3 ML Methods in QSAR for Toxicity
		3 The New Methods of Deep Learning
			3.1 From Neural Networks to Deep Learning
			3.2 Convolutionary NN
				3.2.1 Convolutional Layer
				3.2.2 Pooling Layer
				3.2.3 Fully Connected Layer
			3.3 Inception Network
				3.3.1 Residual Network
		4 Dataset Construction and Preprocessing
			4.1 Ames Test for Mutagenicity and Its Models
			4.2 SMILES and Chemical Graphs
			4.3 Images Generated from Smiles
			4.4 The Dataset
		5 Toxception and Its Results for Mutagenicity
			5.1 Learning and Optimization in Toxception
			5.2 Results of Toxception
		6 Discussion
			6.1 Interpretation and Knowledge Extraction
			6.2 Uncertainty Evaluation
			6.3 Comparison with the State of the Art
			6.4 Pros and Cons of Toxception
		7 Conclusions
		References
	Chapter 7: Use of Machine Learning and Classical QSAR Methods in Computational Ecotoxicology
		1 Ecotoxicology
			1.1 Ecotoxicological Tests
				1.1.1 Acute Toxicity Tests
				1.1.2 Chronic Toxicity Tests
			1.2 Indicators
				1.2.1 Soil Organisms
				1.2.2 Water Organisms
		2 Standardization of Ecotoxicological Tests
			2.1 Data Sets
				2.1.1 OCHEM: Online Chemical Database
					Structure of the Database
					Structure of the Modeling Process
				2.1.2 ECETOC: European Centre for Ecotoxicology and Toxicology of Chemicals (Fig. 2)
				2.1.3 MOA: The Toxic Mode of Action
				2.1.4 ECHA: European Chemicals Agency
				2.1.5 REACH: Registration, Evaluation, Authorization and Restriction of Chemicals
				2.1.6 ECOSAR: Ecological Structure-Activity Relationships Predictive Model
				2.1.7 OECD: The Organisation for Economic Co-operation and Development (Fig. 5)
				2.1.8 US EPA: US Environmental Protection Agency
		3 Machine Learning
			3.1 Machine Learning and QSAR
			3.2 ML Algorithms Applied to QSAR
				3.2.1 Support Vector Machine (SVM)
				3.2.2 Decision Tree and Random Forest
				3.2.3 K-Nearest Neighbor
				3.2.4 Naive Bayes
				3.2.5 Neural Networks
				3.2.6 Ensemble Learning
			3.3 Machine Learning and Ecotoxicology
		4 Conclusion and Perspectives
		Glossary
		References
	Chapter 8: On the Relevance of Feature Selection Algorithms While Developing Non-linear QSARs
		1 Introduction
		2 Feature Selection
		3 Filter Methods
			3.1 Consistency Methods
			3.2 Information Methods
			3.3 Dependency Methods
			3.4 Distance Methods
			3.5 Forward Selection
			3.6 Backward Elimination
			3.7 Stepwise Selection
		4 Wrapper Methods
			4.1 Evolutionary Algorithms
			4.2 Ant Colony Optimization (ACO)
			4.3 Sensitivity Analysis
			4.4 Particle Swarm Optimization
			4.5 Other Methods
		5 Conclusion
		References
	Chapter 9: Got to Write a Classic: Classical and Perturbation-Based QSAR Methods, Machine Learning, and the Monitoring of Nano...
		1 Introduction
		2 Materials
			2.1 Input Data: Collecting and Calculating
			2.2 Protocols for Developing Universal Nanodescriptors
		3 Methods
			3.1 Machine Learning Tools
			3.2 Classical QSAR + ML Models
			3.3 QSAR/QSTR Perturbation Theory Models and Machine Learning
				3.3.1 Mathematics Sustaining QSAR Perturbation Models
				3.3.2 Protocols for QSAR Perturbation + ML Models
				3.3.3 QSAR Perturbation Models and Ecotoxicity Assessment
		4 Conclusions
		References
	Chapter 10: Ecotoxicological QSAR Modeling of Nanomaterials: Methods in 3D-QSARs and Combined Docking Studies for Carbon Nanos...
		1 Introduction
		2 Methods for 3D-QSAR: Overview
		3 3D-QSARs and Combined Docking Studies of Carbon Nanostructured Materials
		4 Notes and Concluding Remarks
		References
	Chapter 11: Early Prediction of Ecotoxicological Side Effects of Pharmaceutical Impurities Based on Open-Source Non-testing Ap...
		1 Introduction
		2 Impurity Definition
		3 Classification of Impurities
		4 Legislation of Impurities
		5 Non-testing Predictive Models
		6 Classification of Predictive Models
			6.1 QSAR
			6.2 Expert Systems
			6.3 Read-Across
		7 T.E.S.T.
		8 VEGA Platform
		9 LAZAR
		10 QSAR Toolbox
			10.1 Input Module
			10.2 Profiling Module
			10.3 Data Module
			10.4 Category Definition Module
			10.5 Data Gap-Filling Module
			10.6 Report Module
		11 Key Factors for a Reliable In Silico Prediction
		12 Problem of False Negatives (FNs)
		13 Limits of Predictive Models
		14 Open-Source Computational Tools
		15 Data Sharing
		16 Consensus Predictions
		17 Conclusion
		References
	Chapter 12: Conformal Prediction for Ecotoxicology and Implications for Regulatory Decision-Making
		1 Introduction
		2 The Workings of a Conformal Predictor
		3 Example of a Conformal Predictor Applied to the Prediction of Chronic Toxicity to Daphnia magna and Pseudokirchneriella subc...
		4 Discussion
		5 Conclusions and Outlook
		References
	Chapter 13: Read-Across for Regulatory Ecotoxicology
		Abbreviations
		1 Introduction
			1.1 Read-Across
			1.2 Regulatory Read-Across, Documentation, and Guidance
			1.3 Use of Read-Across for Industrial and Regulatory Purposes: A Literature Review
			1.4 Available Software and Tools Used in Ecotoxicity-Related Read-Across Predictions
		2 Case Study
		3 Conclusions/Future Prospects
		References
	Chapter 14: Methodological Protocol for Assessing the Environmental Footprint by Means of Ecotoxicological Tools: Wastewater T...
		1 Introduction
		2 Sampling
		3 Sample Preparation
		4 Choice of Bioassays
		5 Bioassays: Materials and Methods
			5.1 Baseline Toxicity
				5.1.1 Algal Growth Inhibition Test
				5.1.2 Bioluminescence Inhibition Test
				5.1.3 Acute Toxicity of Water Flea
				5.1.4 Neutral Red (NR) and MTT Assays
			5.2 Endocrine Disruption
				5.2.1 YES/YAS
				5.2.2 ERE-tk_Luc_MCF-7
			5.3 Genetic Toxicity
				5.3.1 Ames Test
				5.3.2 Single Cell Gel Electrophoresis Assay (SCGE)/Comet Test
				5.3.3 Allium cepa Test
			5.4 Carcinogenic Activity
				5.4.1 Tumor Promotion
				5.4.2 Cell Transformation Assays (CTA)
		6 Elaboration Criteria of Experimental Results
			6.1 Determination of Equivalent Concentration for LCA
			6.2 Direct Use of Results of Bioassays Within the BAD Approach
		7 The Final Step: Facing the Decision Processes
		References
Part III: Case Studies and Literature Reports
	Chapter 15: Development of Baseline Quantitative Structure-Activity Relationships (QSARs) for the Effects of Active Pharmaceut...
		1 Environmental Risk Assessment for Active Pharmaceutical Ingredients
		2 Data Collation
		3 Results
			3.1 Data Analysis
			3.2 Classification of Compounds Using the Verhaar Scheme and Comparison of the Chemical Space of Industrial Compounds Versus A...
		4 Discussion
			4.1 Development of Baseline Toxicity QSARs Relevant to APIs
		5 Conclusions
		References
	Chapter 16: Ecotoxicological QSARs of Personal Care Products and Biocides
		1 Introduction
			1.1 Personal Care Products
			1.2 Biocides
		2 A General Overview of PCPs and Biocides
			2.1 Types of Different PCP and Biocidal Formulations
			2.2 Source of Release into the Environment
			2.3 Risk Assessments of PCPs and Biocides
			2.4 Risk Management of PCPs and Biocides
		3 Application of QSAR in Ecotoxicological Analysis
			3.1 Application of QSAR in Ecotoxicity of PCPs
			3.2 Application of QSAR in Ecotoxicity of Biocides
		4 Prioritized Molecule Among PCPs and Biocides Using QSAR Approach
		5 Conclusion
		References
	Chapter 17: Computational Approaches to Evaluate Ecotoxicity of Biocides: Cases from the Project COMBASE
		1 Introduction
		2 Materials and Methods
			2.1 Activated Sludge QSAR Models
				2.1.1 Organism to Assess and Endpoint to Evaluate
				2.1.2 Data Collection and Definition of a Biocide-Like Space
				2.1.3 Development of the Model
					Qualitative QSAR Model for Activated Sludge
					Quantitative QSAR Model
			2.2 Rainbow Trout QSAR Model
				2.2.1 Data Collection
				2.2.2 Data Cleaning
				2.2.3 Features Selection
				2.2.4 Model Optimization
				2.2.5 Applicability Domain
		3 Results
			3.1 Biocide-Like Chemical Space
			3.2 Qualitative QSAR Model for Activated Sludge
			3.3 Quantitative QSAR Model for Activated Sludge
			3.4 Rainbow Trout QSAR Model
		4 Discussion
		References
	Chapter 18: QSAR Modeling of Dye Ecotoxicity
		1 Introduction
		2 A Short Introduction to QSAR Methodology
		3 Application of QSAR Models to Dye Ecotoxicity
			3.1 QSAR Models for Dye Toxicity
				3.1.1 QSAR Models for Acute Dye Toxicity
				3.1.2 SAR Models for Dye Sensitization
				3.1.3 QSAR Models for Dye Mutagenicity
				3.1.4 QSAR Models for Dye Carcinogenicity
				3.1.5 QSAR Models for Dye Metabolites (Aromatic Amines)
				3.1.6 QSAR Models for Dye Toxicity to Animals and Plants
			3.2 QSAR Models for Dye Ecology
				3.2.1 QSAR Models for Aquatic Toxicity of Dyes
				3.2.2 Environmental Fate and Exposure to Dyes
					QSAR Models for Abiotic Degradation and Decoloration of Dyes
					QSAR Models for Bioelimination and Bioreduction of Dyes
					QSAR Models for Adsorption Removal of Dyes
		4 Conclusions
		References
	Chapter 19: Ecotoxicological QSARs of Mixtures
		1 Introduction
		2 Why Is the Assessment of the Toxicity of Chemical Mixtures Important?
		3 General Principles of the Mixture Toxicity Assessment
			3.1 Whole-Mixture Approach
			3.2 Component-Based Approach
				3.2.1 General Overview
				3.2.2 Similar Action (Dose/Concentration Addition)
				3.2.3 Independent Action (IA)
				3.2.4 Interactions (Synergism and Antagonism)
				3.2.5 Generalized Concentration Addition (GCA) Models
				3.2.6 Realistic Confirmation on the Performance of CA and IA in Ecotoxicological Assessments of Chemical Mixtures
		4 Significance of Chemometric Approaches for Toxicity Assessment of Complex Chemical Mixtures
		5 Quantitative Structure-Activity Relationship (QSAR) Modelling of Ecotoxicity of Mixtures
			5.1 Data Collection and Data Preparation
			5.2 Calculation of Molecular Descriptors
			5.3 Descriptor Selection
			5.4 Modelling Algorithms and Model Validation
		6 Application of QSAR in Ecotoxicity Prediction of Pharmaceutical Mixtures
		7 Application of QSAR in Ecotoxicity Prediction of Mixtures of Agrochemicals
		8 Application of QSAR in Ecotoxicity Prediction of Heavy Metals and Their Mixtures
		9 Application of QSAR in Ecotoxicity Prediction of Organic Chemical Mixtures
		10 Future Perspective
		11 Conclusion
		References
	Chapter 20: QSPR Modeling of Adsorption of Pollutants by Carbon Nanotubes (CNTs)
		1 Introduction
		2 Sources of Pollutants and their Effects
		3 Risk Assessment of Environmental Pollutants
		4 Risk Management of Environmental Pollutants
		5 Carbon Nanotubes (CNTs)
			5.1 Types of Carbon Nanotubes
				5.1.1 Single-Walled CNTs (SWCNTs)
				5.1.2 Multi-walled CNTs (MWCNTs)
			5.2 Application of CNTs
				5.2.1 Structural
				5.2.2 Electromagnetic
				5.2.3 Electroacoustic
				5.2.4 Chemical
			5.3 Role of CNTs as a Nanomaterial in Pollution Management
			5.4 Mechanism of Adsorption of CNTs
		6 Role of Predictive QSPR Models on the Adsorption of CNTs
			6.1 Successful QSPR Modeling of Adsorption of Pollutants by SWCNTs
			6.2 Successful QSPR Modeling of Adsorption of Pollutants by MWCNTs
			6.3 Successful QSPR Modeling of Adsorption of Pollutants by Both SWCNTs and MWCNTs
			6.4 Successful QSPR Modeling of Adsorption of Heavy Metal Ions by MWCNTs
			6.5 Molecular Docking of Organic Compound to CNTs
			6.6 Overview
		7 Conclusion
		References
	Chapter 21: Ecotoxicological QSAR Modeling of Organophosphorus and Neonicotinoid Pesticides
		1 Introduction
		2 Organophosphorus Pesticides: An Overview
		3 The Transition from Organophosphates to Neonicotinoids
		4 Neonicotinoid Pesticides: An Overview
		5 QSAR Models for the Ecotoxicology of Organophosphorus and Neonicotinoid Pesticides
			5.1 QSAR Models for Organophosphorus Aquatic and Terrestrial Organisms Ecotoxicity
			5.2 QSAR Models of Neonicotinoid Terrestrial Organism Ecotoxicity
				5.2.1 QSAR Models of Neonicotinoids with Insecticidal Activity Against Apis mellifera L.
				5.2.2 QSAR Models of Neonicotinoids with Insecticidal Activity Against Musca domestica L. and American Cockroach
				5.2.3 QSAR Models of Neonicotinoids with Insecticidal Activity Against Aphids
			5.3 QSAR Models for the Inhibition Ability of Acetylcholinesterase and Other Enzymes by Organophosphorus (OP) Pesticides
		6 Conclusions
		Glossary
		References
	Chapter 22: QSARs and Read-Across for Thiochemicals: A Case Study of Using Alternative Information for REACH Registrations
		1 Acute Aquatic Toxicity of Thiochemicals for REACH Registrations
		2 Exploratory Data Analysis (EDA): Existing Information and Data Gaps
			2.1 Test Substances and Available Data on Acute Aquatic Toxicity
			2.2 Toxicological and Chemical Grouping of Thiochemicals and Mode of Action (MoA)
			2.3 Physicochemical Descriptors of Thiochemicals
			2.4 Data Gaps
		3 QSARs, Read-Across, and Testing Strategies for Acute Aquatic Toxicity of Thiochemicals
			3.1 QSARs
			3.2 Read-Across
				3.2.1 Thiolactic Acid (TLA)
				3.2.2 Glycol Dimercaptoacetate (GDMA)
				3.2.3 2-Ethylhexyl 3-Mercaptopropionate (EHMP)
				3.2.4 Thioglycerol (TG)
				3.2.5 Lauryl/Stearyl Thiodipropionate (E1218)
			3.3 Overall Assessment of the Available Information
		4 Implications of Data Quality
			4.1 Structural and Functional Similarity
			4.2 Experimental Difficulties and Variability of Source Data
			4.3 ITS and WoE
			4.4 Further Tests
		References
	Chapter 23: In Silico Ecotoxicological Modeling of Pesticide Metabolites and Mixtures
		1 Introduction: The Ecotoxicity of Transformation Products
		2 Integrated Software for Modeling Metabolites
		3 Multimedia Multi-species Models
		4 The Metabolic Pathway Software System MetaPath
		5 Prediction Models for the Human Toxicity of Transformation Products
		6 Quantitative Structure-Activity Relationship Models for Transformation Products
		7 Quantum-Chemical Modeling Methods for Transformation Products
		8 Mixture Toxicity of Pesticides
		9 Concentration Addition (CA) and Independent Action (IA) Modeling
		10 Modeling Deviation
		11 Computational Approach to the Toxicity Assessment
		12 Quantitative Structure-Activity Relationship (QSAR) Modeling
		13 QSAR Modeling Based on Chemical Reactivity Theory
		14 Conclusions
		References
	Chapter 24: Combination of Read-Across and QSAR for Ecotoxicity Prediction: A Case Study of Green Algae Growth Inhibition Toxi...
		1 Introduction
		2 Data Preparation
		3 Methods
		4 Results and Discussion
			4.1 Pre-screening
			4.2 Step 1: Interspecies QSAR
			4.3 Step 2: QSARs for Nonpolar and Polar Narcotic Chemicals
			4.4 Step 3: Categorizations for Read-Across
		5 Conclusions
		6 Note: Information
		References
	Chapter 25: QSAR Approaches and Ecotoxicological Risk Assessment
		1 Introduction
		2 State of the Art on Pollution and Its Negative Impacts
			2.1 Ecosystem Pollution
			2.2 Adverse Effects of Pollutants
		3 Review of Literature on QSAR Ecotoxicity Modeling
			3.1 QSAR Models for Ecotoxicity Prediction of Pesticides
			3.2 QSAR Models for Ecotoxicity Prediction of Ionic Liquids
			3.3 QSAR Models for Ecotoxicity Prediction of Pharmaceuticals
			3.4 QSAR Models for Ecotoxicity Prediction of Other Pollutants
		4 Conclusions
		References
	Chapter 26: Multi-scale QSAR Approach for Simultaneous Modeling of Ecotoxic Effects of Pesticides
		1 Introduction
		2 Materials and Methods
			2.1 Construction of the Dataset and Calculation of the Molecular Descriptors
			2.2 Development of the ms-QSAR Model
		3 Results and Discussion
			3.1 The ms-QSAR-ANN Model
			3.2 Applicability Domain
			3.3 Interpretation of the Molecular Descriptors
		4 Conclusions
		References
	Chapter 27: Quantitative Structure-Toxicity Relationship Models Based on Hydrophobicity and Electrophilicity
		1 Introduction
		2 Theory
		3 Method
			3.1 Computational Details
			3.2 Regression Analysis
				3.2.1 Pimephales promelas
				3.2.2 Tetrahymena pyriformis
		4 Conclusion
		References
	Chapter 28: Environmental Toxicity (Q)SARs for Polymers as an Emerging Class of Materials in Regulatory Frameworks, with a Foc...
		1 Introduction
			1.1 Current Regulatory View of Polymers
		2 Materials and Methods
			2.1 Compounds: Polymers-A Brief Overview of Chemical Diversity and Available (Q)SARs
			2.2 Cationic Polymers
			2.3 Polyquaternium Cationic Polymers: A Complex Cationic Polymer Category
		3 (Q)SAR Methods
			3.1 Chemometric Tools in Ecotoxicological Evaluation of Polymers
			3.2 (Q)SAR Methodologies: Broad Classifications
			3.3 Protocols for (Q)SAR Analysis in Polymers
		4 Applications of (Q)SAR to Polymers: A Literature Review-Applications of (Q)SAR in Ecotoxicity of Polymers
			4.1 Application of (Q)SAR in Toxicity Prediction of Polymers (Peptides)
			4.2 Application of (Q)SAR to Biomedical Applications of Polymers
			4.3 Applications of (Q)SAR in Property Estimation of Polymers
		5 Discussion of Future Avenues: Application of Fragment-Based (Q)SAR and Read-Across in Ecotoxicity Predictions of Polymers
		6 Conclusions
		References
Part IV: Tools, Databases, and Web Servers
	Chapter 29: Ecotoxicity Databases for QSAR Modeling
		1 Introduction
		2 Ecotoxicity
		3 Role of the QSAR Model in Ecotoxicity Evaluation
		4 Toxicity and Ecotoxicity Databases
			4.1 Aggregated Computational Toxicology Online Resource (ACToR)
			4.2 Birth Defects Systems Manager (BDSM)
			4.3 Carcinogenic Potency Database (CPDB)
			4.4 Chemical Carcinogenesis Research Information System (CCRIS)
			4.5 Danish (Q)SAR Database
			4.6 Developmental and Reproductive Toxicology Database (DART)
			4.7 Developmental Toxicity (DevTox)
			4.8 Distributed Structure-Searchable Toxicity Database (DSSTox)
			4.9 ECOTOXicology Knowledgebase (ECOTOX)
			4.10 European Chemical Substances Information System (ESIS)
			4.11 Extension TOXicology NETwork (EXTOXNET)
			4.12 eTox
			4.13 Fraunhofer RepDose
			4.14 Genetic Alterations in Cancer (GAC)
			4.15 Genetic Activity Profile (GAP)
			4.16 Gene-Tox
			4.17 Human and Environmental Risk Assessment (HERA)
			4.18 Hazard Evaluation Support System (HESS) Attached Database (HESS DB)
			4.19 Hazardous Substances Data Bank (HSDB)
			4.20 International Agency for Research on Cancer (IARC) Monograph
			4.21 Integrated Risk Information System (IRIS)
			4.22 International Toxicity Estimates for Risk (ITER)
			4.23 Japan Existing Chemical Database (JECDB)
			4.24 Leadscope
			4.25 MDL
			4.26 National Toxicology Program (NTP)
			4.27 Organization for Economic Cooperation and Development (OECD)
			4.28 Optimized Strategies for Risk Assessment of Industrial Chemicals Through Integration of Non-test and Test Information (OS...
			4.29 Risk Assessment Information System (RAIS)
			4.30 Risk Information Exchange (RiskIE)
			4.31 Registry of Industrial Toxicology Animal-Data (RITA)
			4.32 Toxicology Testing in the 21st Century (Tox21)
			4.33 ToxCast
			4.34 TOXMAP
			4.35 Toxicology Data Network (TOXNET)
			4.36 Toxicity Reference Database (ToxRefDB)
			4.37 Toxic Substances Control Act Test Submissions (TSCATS)
			4.38 US FDA Chemical Evaluation and Risk Estimation System (CERES)
			4.39 VITIC
			4.40 WikiPharma
		5 Application of Ecotoxicity Databases
		6 Future Avenues and Conclusion
		Glossary
		References
	Chapter 30: VEGAHUB for Ecotoxicological QSAR Modeling
		1 Introduction
		2 The Different Tools Available Within VEGAHUB
			2.1 The VEGA QSAR Models
			2.2 The Tool for Applicability Domain
			2.3 The Use of VEGA for Read Across
			2.4 ToxRead
			2.5 ToxWeight
			2.6 PROMETHUES and JANUS
			2.7 Research Tools for Modeling
		3 Examples of Use of the VEGA Platform
			3.1 Example 1: Glycolic Acid, Fish Acute Toxicity
			3.2 Example 2: 2-(4-Terbutylbenzyl)Propionaldehyde, Fish Acute Toxicity
		4 The Future Tools
			4.1 The toDIVINE Project
			4.2 The LIFE VERMEER Project
			4.3 The LIFE CONCERT REACH Project
			4.4 The OptiTox Project
			4.5 Endocrine Disruption
		5 Conclusions
		References
	Chapter 31: Enalos Cloud Platform: Nanoinformatics and Cheminformatics Tools
		1 Introduction
		2 MouseTox
			2.1 Initiating the Analysis
			2.2 Produced Results
		3 A Safe-by-Design Tool for Functionalized Nanomaterials
			3.1 Initiating the Analysis
			3.2 Produced Results
		4 Protein Corona Fingerprints Tool for the Virtual Screening of Gold Nanoparticle Cellular Association
			4.1 Initiating the Analysis
			4.2 Produced Results
		5 Conclusions
		References
	Chapter 32: alvaDesc: A Tool to Calculate and Analyze Molecular Descriptors and Fingerprints
		1 Introduction
		2 Molecular Structure Curation and Standardization
		3 Molecular Descriptors
			3.1 Molecular Descriptor Analysis and Interpretation
			3.2 Variable Reduction
		4 Structural Keys and Molecular Fingerprints
			4.1 Structural Keys
			4.2 Hashed Chemical Fingerprints
		5 Dealing with Disconnected Structures
		6 Conclusions
		References
Index