This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.

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 book provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting. Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence.

Dieses Buch ist ein wichtiges Referenzwerk für Toxikologen in vielen Bereichen und bietet eine umfassende Analyse molekular Modellansätze und Strategien der Risikobewertung von pharmazeutischen und Umweltchemikalien. - Zeigt, was mit rechnergestützter Toxikologie aktuell erreicht werden kann, und wirft einen Blick auf zukünftige Entwicklungen. - Gibt Antworten zu Themen wie Datenquellen, Datenpflege, Behandlung, Modellierung und Interpretation kritischer Endpunkte im Hinblick auf Gefahrenbewertungen im 21. Jahrhundert. - Bündelt herausragende Konzepte und das Wissen führender Autoren in einem einzigartigen Referenzwerk. - Untersucht detailliert QSAR-Modelle, Eigenschaften physiochemischer Arzneistoffe, strukturbasiertes Drug Targeting, die Bewertung chemischer Mischungen und Umweltmodelle. - Behandelt zusätzlich die Sicherheitsbewertung von Verbraucherprodukten und den Bereich chemische Abwehr und bietet Kapitel zu Open-Source-Toxikologie und Big Data.

This volume explores techniques that are currently used to understand solid target-specific models in computational toxicology. The chapters are divided into four sections and discuss topics such as molecular descriptors, QSAR and read-across; molecular and data modeling techniques to comply with both scientific and regulatory sides; computational toxicology in drug discovery; and strategies on how to predict various human-health toxicology endpoints. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the methods and software tools used, step-by-step, readily reproducible computational protocols, and tips on troubleshooting and avoiding known pitfalls. Comprehensive and cutting-edge, Computational Toxicology: Methods and Protocols is a valuable resource for researchers who are interested in learning more about this expanding field.

Computational Toxicology for Drug Safety and a Sustainable Environment is a primer on computational techniques in environmental toxicology for scholars. The book presents 9 in-depth chapters authored by expert academicians and scientists aimed to give readers an understanding of how computational models, software and algorithms are being used to predict toxicological profiles of chemical compounds. The book also aims to help academics view toxicological assessment from the lens of sustainability by providing an overview of the recent developments in environmentally-friendly practices. The chapters review the strengths and weaknesses of the existing methodologies, and cover new developments in computational tools to explain how researchers aim to get accurate results. Each chapter features a simple introduction and list of references to benefit a broad range of academic readers. List of topics: 1. Applications of computational toxicology in pharmaceuticals, environmental and industrial practices 2. Verification, validation and sensitivity studies of computational models used in toxicology assessment 3. Computational toxicological approaches for drug profiling and development of online clinical repositories 4. How to neutralize chemicals that kill environment and humans: an application of computational toxicology 5. Adverse environmental impact of pharmaceutical waste and its computational assessment 6. Computational aspects of organochlorine compounds: DFT study and molecular docking calculations 7. In-silico studies of anisole and glyoxylic acid derivatives 8. Computational toxicology studies of chemical compounds released from firecrackers 9. Computational nanotoxicology and its applications Readership Graduate and postgraduate students, academics and researchers in pharmacology, computational biology, toxicology and environmental science programs.