This volume focuses on computational modeling of the ecotoxicity of chemicals and presents applications of quantitative structure–activity relationship models (QSARs) in the predictive toxicology field in a regulatory context. The extensive book covers a variety of protocols for descriptor computation, data curation, feature selection, learning algorithms, validation of models, applicability domain assessment, confidence estimation for predictions, and much more, as well as case studies and literature reviews on a number of hot topics. Written for the Methods in Pharmacology and Toxicology series, chapters include the kind of practical advice that is essential for researchers everywhere. Authoritative and comprehensive, Ecotoxicological QSARs is an ideal source to update readers in the field with current practices and introduce to them new developments and should therefore be very useful for researchers in academia, industries, and regulatory bodies.

QSAR and SPECTRAL-SAR in Computational Ecotoxicology presents a collection of studies based on the epistemological bulk data-information-knowledge of the chemicals used in green chemistry. It assesses a specific model of pattern characterization of concerned active substances at the bio-, eco-, and pharmacologic levels through unitary formulation of the effector-receptor binding degree potential, including the logistic type by employing a computational algebraic quantitative structure-activity relationship (QSAR) model called SPECTRAL-SAR. It aims to minimize the residual recorded activities in the experiments that study the enzymic, ionic liquid, antagonists, and allosteric inhibition interactions. The book covers: The classic QSAR approach The new SPECTRAL-SAR approach How to draw SPECTRAL-SAR maps for predicting ecotoxicological mechanisms for a given series of toxicants and single or multi-species in an open system Biological activity as related to chemical reactivity through associate descriptors This book will be very useful in advanced courses on computational ecotoxicology, drug design and interaction, methods in quantum and computational chemistry, chemical molding, chemical bonding, and others.

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.

This brief goes back to basics and describes the Quantitative structure-activity/property relationships (QSARs/QSPRs) that represent predictive models derived from the application of statistical tools correlating biological activity (including therapeutic and toxic) and properties of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure and/or properties. It explains how the sub-discipline of Cheminformatics is used for many applications such as risk assessment, toxicity prediction, property prediction and regulatory decisions apart from drug discovery and lead optimization. The authors also present, in basic terms, how QSARs and related chemometric tools are extensively involved in medicinal chemistry, environmental chemistry and agricultural chemistry for ranking of potential compounds and prioritizing experiments. At present, there is no standard or introductory publication available that introduces this important topic to students of chemistry and pharmacy. With this in mind, the authors have carefully compiled this brief in order to provide a thorough and painless introduction to the fundamental concepts of QSAR/QSPR modelling. The brief is aimed at novice readers.

Structure-Activity Relationships in Environmental Science is the first book of its kind that brings together information from a variety of sources into one document. It provides a comprehensive overview of the entire field of quantitative structure-activity relationships (QSARs) as well as being a reference for SAR experts. The book comprises three parts. Part One covers the theoretical background of structure-activity studies and Part Two deals with the practical applications of such methods in the environmental sciences. Part Three critically discusses SAR models with respect to their reliability and their aptness in environmental hazard and risk assessment. Recommendations are made as to which model to use and the case is presented for using QSARs in hazard assessment. The use of QSARs is becoming increasingly important since there is little experimental data available on environmentally relevant chemicals. Structure-Activity Relationships in Environmental Sciences will thus serve as an invaluable guide to both postgraduate and research scientists as well as professional ecologists.