Ecotoxicological Qsars

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 o

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.

Ever since Rachel Carson's Silent Spring, we have generally become aware of environmental contaminants and their effects on the ecosystem. The findin~ of PCB's in fish by Soren Jensen in Sweden, the recognition of mirex as contaminant in fish from Lake Ontario, and the discoveries of contaminant laden leachates from dumpsites such as the Love Canal have become milestones in the search for and charac terization of contaminants in our environment. At this time, the problem no longer is so much the identifi cation of contaminants and their sources. Rather, we are now faced with solving questions on the fates and effects of such compounds. This includes the search for mechanisms to deal effectively with the large number of chemicals already found in water, air and biota. One of such time and cost saving scientific avenues is the field of quantitative structure-activity correlations for the prediction of the environmental behavior and effects of compounds.

Historically, regulations governing chemical use have often focused on widely used chemicals and acute human health effects of exposure to them, as well as their potential to cause cancer and other adverse health effects. As scientific knowledge has expanded there has been an increased awareness of the mechanisms through which chemicals may exert harmful effects on human health, as well as their effects on other species and ecosystems. Identification of high-priority chemicals and other chemicals of concern has prompted a growing number of state and local governments, as well as major companies, to take steps beyond existing hazardous chemical federal legislation. Interest in approaches and policies that ensure that any new substances substituted for chemicals of concern are assessed as carefully and thoroughly as possible has also burgeoned. The overarching goal of these approaches is to avoid regrettable substitutions, which occur when a toxic chemical is replaced by another chemical that later proved unsuitable because of persistence, bioaccumulation, toxicity, or other concerns. Chemical alternative assessments are tools designed to facilitate consideration of these factors to assist stakeholders in identifying chemicals that may have the greatest likelihood of harm to human and ecological health, and to provide guidance on how the industry may develop and adopt safer alternatives. A Framework to Guide Selection of Chemical Alternatives develops and demonstrates a decision framework for evaluating potentially safer substitute chemicals as primarily determined by human health and ecological risks. This new framework is informed by previous efforts by regulatory agencies, academic institutions, and others to develop alternative assessment frameworks that could be operationalized. In addition to hazard assessments, the framework incorporates steps for life-cycle thinking - which considers possible impacts of a chemical at all stages including production, use, and disposal - as well as steps for performance and economic assessments. The report also highlights how modern information sources such as computational modeling can supplement traditional toxicology data in the assessment process. This new framework allows the evaluation of the full range of benefits and shortcomings of substitutes, and examination of tradeoffs between these risks and factors such as product functionality, product efficacy, process safety, and resource use. Through case studies, this report demonstrates how different users in contrasting decision contexts with diverse priorities can apply the framework. This report will be an essential resource to the chemical industry, environmentalists, ecologists, and state and local governments.

Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development aims at showcasing different structure-based, ligand-based, and machine learning tools currently used in drug design. It also highlights special topics of computational drug design together with the available tools and databases. The integrated presentation of chemometrics, cheminformatics, and machine learning methods under is one of the strengths of the book.The first part of the content is devoted to establishing the foundations of the area. Here recent trends in computational modeling of drugs are presented. Other topics present in this part include QSAR in medicinal chemistry, structure-based methods, chemoinformatics and chemometric approaches, and machine learning methods in drug design. The second part focuses on methods and case studies including molecular descriptors, molecular similarity, structure-based based screening, homology modeling in protein structure predictions, molecular docking, stability of drug receptor interactions, deep learning and support vector machine in drug design. The third part of the book is dedicated to special topics, including dedicated chapters on topics ranging from de design of green pharmaceuticals to computational toxicology. The final part is dedicated to present the available tools and databases, including QSAR databases, free tools and databases in ligand and structure-based drug design, and machine learning resources for drug design. The final chapters discuss different web servers used for identification of various drug candidates. Presents chemometrics, cheminformatics and machine learning methods under a single reference Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design Highlights special topics of computational drug design and available tools and databases

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.

Second in our Ecotoxicology series, this book presents a timely discussion of theoretical and practical issues involved in the study of ecotoxicology. By concentrating on the key issues, the book provides an exciting introduction for those new to ecotoxicology while stimulating veterans in the field into lively debate.