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Titlebook: In Silico Methods for Predicting Drug Toxicity; Emilio Benfenati Book 2016 Springer Science+Business Media New York 2016 Pharmaceutical mo

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發(fā)表于 2025-3-21 17:31:18 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱In Silico Methods for Predicting Drug Toxicity
編輯Emilio Benfenati
視頻videohttp://file.papertrans.cn/463/462972/462972.mp4
概述Includes cutting-edge methods and protocols involving in silico techniques.Provides step-by-step detail essential for reproducible results.Contains key notes and implementation advice from the experts
叢書名稱Methods in Molecular Biology
圖書封面Titlebook: In Silico Methods for Predicting Drug Toxicity;  Emilio Benfenati Book 2016 Springer Science+Business Media New York 2016 Pharmaceutical mo
描述.This detailed volume explores in silico methods forpharmaceutical toxicity by combiningthe theoretical advanced research with the practical application of the tools.Beginning with a section covering sophisticated models addressing the bindingto receptors, pharmacokinetics and adsorption, metabolism, distribution, andexcretion, the book continues with chapters delving into models for specifictoxicological and ecotoxicological endpoints, as well as broad views of themain initiatives and new perspectives which will very likely improve our way ofmodelling pharmaceuticals. Written for the highly successful .Methods in Molecular Biology. series,chapters include the kind of detailed implementation advice that is key forachieving successful research results...Authoritative and practical, In Silico Methods for Predicting DrugToxicity offers the advantage of incorporating data and knowledge fromdifferent fields, such as chemistry, biology, -omics, and pharmacology, toachieve goals in this vital area of research..
出版日期Book 2016
關(guān)鍵詞Pharmaceutical modeling; In silico models; Toxicological endpoints; Receptor binding; Pharmacokinetics; C
版次1
doihttps://doi.org/10.1007/978-1-4939-3609-0
isbn_softcover978-1-4939-8093-2
isbn_ebook978-1-4939-3609-0Series ISSN 1064-3745 Series E-ISSN 1940-6029
issn_series 1064-3745
copyrightSpringer Science+Business Media New York 2016
The information of publication is updating

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In Silico 3D Modeling of Binding Activitiesr homology modeling of receptors have been reliably used in pharmacological research and development for decades. Molecular docking methodologies are helpful for revealing facets of activation and inactivation, thus improving mechanistic understanding and predicting molecular ligand binding activity
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Modeling Pharmacokineticsescriptions of xenobiotics’ absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mecha
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In Silico Prediction of Chemically Induced Mutagenicity: How to Use QSAR Models and Interpret Their AR models that can predict Ames genotoxicity are freely available for download from the Internet and they can provide relevant information for the toxicological profiling of chemicals. Indeed, they can be straightforwardly used for predicting the presence or absence of genotoxic hazards associated w
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In Silico Methods for Carcinogenicity Assessmentative predictive models, ranging from short-term biological assays (e.g. mutagenicity tests) to theoretical models, have been attempted in this field. Theoretical approaches such as (Q)SAR are highly desirable for identifying carcinogens, since they actively promote the replacement, reduction, and r
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In Silico Models for Repeated-Dose Toxicity (RDT): Prediction of the No Observed Adverse Effect Leve is the determination of the no observed adverse effect level (NOAEL) and the lowest observed adverse effect level (LOAEL). NOAEL is important since it serves to calculate the maximum recommended starting dose (MRSD) which is the safe starting dose for clinical studies in human beings. Since in vivo
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發(fā)表于 2025-3-23 08:37:31 | 只看該作者
In Silico Models for Acute Systemic Toxicityvailability of structure-based computational models that are available and potentially useful in the assessment of acute systemic toxicity. The most recently published literature models for acute systemic toxicity are also discussed, and perspectives for future developments in this field are offered
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