Machine Learning and Deep Learning in Computational Toxicology PDF Download
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Author: Huixiao Hong
Publisher: Springer Nature
ISBN: 3031207300
Category : Medical
Languages : en
Pages : 654
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Book Description
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.
Author: Huixiao Hong
Publisher: Springer Nature
ISBN: 3031207300
Category : Medical
Languages : en
Pages : 654
Get Book Here
Book Description
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.
Author: Kunal Roy
Publisher: John Wiley & Sons
ISBN: 1119681596
Category : Science
Languages : de
Pages : 596
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Book Description
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.
Author: Niklas Lidströmer
Publisher: Springer
ISBN: 9783030645724
Category : Medical
Languages : en
Pages : 1816
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Book Description
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.
Author: Sean Ekins
Publisher: John Wiley & Sons
ISBN: 111928256X
Category : Science
Languages : en
Pages : 450
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Book Description
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.
Author: Orazio Nicolotti
Publisher: Springer Nature
ISBN: 1071640038
Category :
Languages : en
Pages : 441
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Book Description
Author: Orazio Nicolotti
Publisher: Humana Press
ISBN: 9781493978984
Category : Medical
Languages : en
Pages : 439
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Book Description
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.
Author: Tahmeena Khan, Saman Raza
Publisher: Bentham Science Publishers
ISBN: 9815196995
Category : Science
Languages : en
Pages : 233
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Book Description
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.
Author: Luis G. Valerio, Jr.
Publisher: CRC Press
ISBN: 1040101836
Category : Science
Languages : en
Pages : 253
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Book Description
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.
Author: Mark T. D. Cronin
Publisher: Royal Society of Chemistry
ISBN: 1849730040
Category : Medical
Languages : en
Pages : 689
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Book Description
This book defines the use of computational approaches to predict the environmental toxicity and human health effects of organic chemicals.
Author: Ruili Huang
Publisher: Frontiers Media SA
ISBN: 2889451976
Category :
Languages : en
Pages : 104
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Book Description
Tens of thousands of chemicals are released into the environment every day. High-throughput screening (HTS) has offered a more efficient and cost-effective alternative to traditional toxicity tests that can profile these chemicals for potential adverse effects with the aim to prioritize a manageable number for more in depth testing and to provide clues to mechanism of toxicity. The Tox21 program, a collaboration between the National Institute of Environmental Health Sciences (NIEHS)/National Toxicology Program (NTP), the U.S. Environmental Protection Agency’s (EPA) National Center for Computational Toxicology (NCCT), the National Institutes of Health (NIH) National Center for Advancing Translational Sciences (NCATS), and the U.S. Food and Drug Administration (FDA), has generated quantitative high-throughput screening (qHTS) data on a library of 10K compounds, including environmental chemicals and drugs, against a panel of nuclear receptor and stress response pathway assays during its production phase (phase II). The Tox21 Challenge, a worldwide modeling competition, was launched that asks a “crowd” of researchers to use these data to elucidate the extent to which the interference of biochemical and cellular pathways by compounds can be inferred from chemical structure data. In the Challenge participants were asked to model twelve assays related to nuclear receptor and stress response pathways using the data generated against the Tox21 10K compound library as the training set. The computational models built within this Challenge are expected to improve the community’s ability to prioritize novel chemicals with respect to potential concern to human health. This research topic presents the resulting computational models with good predictive performance from this Challenge.
