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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.
Introduction
Water Quality and Contaminants of Emerging Concern (CECs)
The Effects of Contaminants of Emerging Concern on Water Quality
Chemometrics: Multivariate Statistical Analysis of Analytical Chemical and Biomolecular Data
An Introduction to Chemometrics and Cheminformatics
Chemometric and Cheminformatic Tools and Protocols
An Introduction to Some Basic Chemometric Tools
From Data to Models: Mining Experimental Values with Machine Learning Tools
Machine Learning Approaches in Computational Toxicology Studies
Counter-Propagation Neural Networks for Modeling and Read Across in Aquatic (Fish) Toxicity
Aiming High versus Aiming All: Aquatic Toxicology and QSAR Multitarget Models
Chemometric Approaches to Evaluate Interspecies Relationships and Extrapolation in Aquatic Toxicity
Case Studies and Literature Reports
The QSAR Paradigm to Explore and Predict Aquatic Toxicity
Application of Cheminformatics to Model Fish Toxicity
Chemometric Modeling of Algal and Daphnia Toxicity
Chemometric Modeling of Algal Toxicity
Chemometric Modeling of Daphnia Toxicity
Chemometric Modeling of Daphnia Toxicity: Quantum-Mechanical Insights
Chemometric Modeling of Toxicity of Chemicals to Tadpoles
Chemometric Modeling of Toxicity of Chemicals to Marine Bacteria
Chemometric Modeling of Pesticide Aquatic Toxicity
Contribution of Chemometric Modeling to Chemical Risks Assessment for Aquatic Plants: State-of-the-Art
Application of 3D-QSAR Approaches to Classification and Prediction of Aquatic Toxicity
QSAR Modeling of Aquatic Toxicity of Cationic Polymers
Tools and Databases
In Silico Platforms for Predictive Ecotoxicology: From Machine Learning to Deep Learning
The Tools for Aquatic Toxicology within the VEGAHUB System
Aquatic Toxicology Databases
Computational Tools for the Assessment and Substitution of Biocidal Active Substances of Ecotoxicological Concern: The LIFE-COMBASE
Image Analysis and Deep Learning Web Services for Nano informatics