The rate at which toxicological data is generated is continually becoming more rapid and the volume of data generated is growing dramatically. This is due in part to advances in software solutions and cheminformatics approaches which increase the availability of open data from chemical, biological and toxicological and high throughput screening resources. However, the amplified pace and capacity of data generation achieved by these novel techniques presents challenges for organising and analysing data output. Big Data in Predictive Toxicology discusses these challenges as well as the opportunities of new techniques encountered in data science. It addresses the nature of toxicological big data, their storage, analysis and interpretation. It also details how these data can be applied in toxicity prediction, modelling and risk assessment. This title is of particular relevance to researchers and postgraduates working and studying in the fields of computational methods, applied and physical chemistry, cheminformatics, biological sciences, predictive toxicology and safety and hazard assessment.

Predictive data science is already in use in many fields, but its application in toxicology is new and sought after by non-animal alternative testing initiatives. Predictive Analytics for Toxicology: Applications in Discovery Science provides a comprehensive overview of the application of predictive analytics in the field of toxicology, highlighting its role and applications in discovery science. This book addresses the challenges of accurately predicting high-level endpoints of toxicity and explores the use of computational and artificial intelligence research to automate predictive toxicology. It underscores the importance of predictive toxicology in proposing and explaining adverse outcomes resulting from human exposures to specific toxicants, especially when experimental and observational data on the toxicant are incomplete or unavailable. Key features: Includes a plain language description of predictive analytics in toxicology adding an overview of the wide range of applications Examines the science of prediction, computational models as an automated science and comprehensive discussions on concepts of machine learning Opens the hood on AI and its applications in toxicology Features coverage on how in silico toxicity predictions are translational science tools The book integrates strategies and practices of predictive toxicology and offers practical information that students and professionals of the toxicology, chemical, and pharmaceutical industries will find essential. It fulfills the expectations of student researchers seeking to learn predictive analytics in toxicology. This book will energize scientists to conduct predictive toxicology modeling using artificial intelligence and machine learning, and inspire students and seasoned scientists interested in automated science to pick up new research using predictive in silico models to evaluate chemical-induced toxicity. With its focus on practical applications and real-world examples, this book serves as a guide for navigating the complex issues and practices of discovery toxicology. It is an essential resource for those interested in computer-based methods in toxicology, providing valuable insights into the use of predictive analytics.

A comprehensive overview of techniques and systems currently utilized in predictive toxicology, this reference presents an in-depth survey of strategies to characterize chemical structures and biological systems—covering prediction methods and algorithms, sources of high-quality toxicity data, the most important commercial and noncommercial predictive toxicology programs, and advanced technologies in computational chemistry and biology, statistics, and data mining.

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.

Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology provides an up-to-date presentation of big data analytics methods and their applications in diverse fields. The proper management of big data for decision-making in scientific and social issues is of paramount importance. This book gives researchers the tools they need to solve big data problems in these fields. It begins with a section on general topics that all readers will find useful and continues with specific sections covering a range of interdisciplinary applications. Here, an international team of leading experts review their respective fields and present their latest research findings, with case studies used throughout to analyze and present key information. Brings together the current knowledge on the most important aspects of big data, including analysis using deep learning and fuzzy logic, transparency and data protection, disparate data analytics, and scalability of the big data domain Covers many applications of big data analysis in diverse fields such as chemistry, chemoinformatics, bioinformatics, computer-assisted drug/vaccine design, characterization of emerging pathogens, and environmental protection Highlights the considerable benefits offered by big data analytics to science, in biomedical fields and in industry

A comprehensive overview of techniques and systems currently utilized in predictive toxicology, this reference presents an in-depth survey of strategies, algorithms, and prediction methods to select, calculate, and represent the features and properties of chemical structures in biological systems. It provides sources of high-quality toxicity data, the most important commercial and noncommercial predictive toxicology programs, and advanced technologies in computational chemistry, biology, statistics, and data mining. Predictive Toxicology explores applications that go beyond classical structure-activity relationships and discusses programs such as OncoLogic, META, MC4PC, PASS, and lazar.

Environmental Nanotoxicology: Combatting the Minute Contaminants is a comprehensive guide to the rapidly evolving field of nanotoxicology and its implications for environmental health and safety. This book results from the collaborative efforts of leading experts and researchers from diverse disciplines, aiming to thoroughly understand the interactions between nanomaterials and the environment and their potential impacts on the delicate balance of our ecosystems. Nanotechnology has witnessed remarkable innovations leading to the development of nanomaterials with novel properties and applications across various industries. Alongside these innovations, concerns have arisen about the potential risks that nanomaterials may pose to the environment and living organisms. This book addresses these concerns by comprehensively exploring the field's key concepts, principles, and methodologies. It includes case studies and offers insights into developing appropriate regulatory frameworks and guidelines for the responsible use and disposal of nanomaterials. The book is a valuable resource for researchers and professionals working in nanotoxicology on the complex challenges posed by the intersection of nanomaterials and the environment. It is also an essential reference for students studying environmental science, toxicology, and nanotechnology. Addresses risk assessment and management in nanotoxicology; Explores the life cycle assessment of nanoparticles; Sheds light on emerging technologies and future directions in environmental nanotoxicology. .