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The book covers theoretical background and methodology as well as all current applications of Quantitative Structure-Activity Relationships (QSAR). Written by an international group of recognized researchers, this edited volume discusses applications of QSAR in multiple disciplines such as chemistry, pharmacy, environmental and agricultural sciences addressing data gaps and modern regulatory requirements. Additionally, the applications of QSAR in food science and nanoscience have been included – two areas which have only recently been able to exploit this versatile tool.   This timely addition to the series is aimed at graduate students, academics and industrial scientists interested in the latest advances and applications of QSAR.

Chemistry. --- Pharmaceutical technology. --- Food --- Chemistry, Physical and theoretical. --- Medicinal chemistry. --- Agriculture. --- Environmental chemistry. --- Theoretical and Computational Chemistry. --- Pharmaceutical Sciences/Technology. --- Environmental Chemistry. --- Food Science. --- Medicinal Chemistry. --- Biotechnology. --- QSAR (Biochemistry) --- Quantitative structure-activity relationships (Biochemistry) --- Structure-activity relationships (Biochemistry) --- Food science. --- Biochemistry. --- Farming --- Husbandry --- Industrial arts --- Life sciences --- Food supply --- Land use, Rural --- Biological chemistry --- Chemical composition of organisms --- Organisms --- Physiological chemistry --- Biology --- Chemistry --- Medical sciences --- Science --- Chemistry, Environmental --- Ecology --- Pharmaceutical laboratory techniques --- Pharmaceutical laboratory technology --- Technology, Pharmaceutical --- Technology --- Physical sciences --- Composition --- Food—Biotechnology. --- Chemistry, Medical and pharmaceutical --- Chemistry, Pharmaceutical --- Drug chemistry --- Drugs --- Medical chemistry --- Medicinal chemistry --- Pharmacochemistry --- Chemistry, Theoretical --- Physical chemistry --- Theoretical chemistry

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This volume describes different computational methods encompassing ligand-based approaches (QSAR, pharmcophore), structure-based approaches (homology modeling, docking, molecular dynamics simulation), and combined approaches (virtual screening) with applications in anti-Alzheimer drug design. Different background topics like molecular etiologies of Alzheimer’s disease, targets for new drug development, and different cheminformatic modeling strategies are covered for completeness. Special topics like multi-target drug development, natural products, protein misfolding, and nanomaterials are also included in connection with computational modeling of anti-Alzheimer drug development. In Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Cutting-edge and authoritative, Computational Modeling of Drugs Against Alzheimer’s Disease is a valuable resource for learning about the latest computational techniques used to study this disease.

Neurosciences. --- Neural sciences --- Neurological sciences --- Neuroscience --- Medical sciences --- Nervous system

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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.

Pharmacology. --- Environmental chemistry. --- Bioinformatics. --- Environmental Chemistry. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Chemistry, Environmental --- Chemistry --- Ecology --- Drug effects --- Medical pharmacology --- Medical sciences --- Chemicals --- Chemotherapy --- Drugs --- Pharmacy --- Data processing --- Physiological effect

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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.

Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Drugs --- Drug development. --- QSAR (Biochemistry) --- Cheminformatics. --- Machine learning --- Quantitative Structure-Activity Relationship --- Cheminformatics --- Machine Learning --- Drug Design --- Structure-activity relationships. --- Therapeutic use. --- Quantitative Structure-Activity Relationship. --- Machine Learning. --- Drug Design.

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In Silico Drug Design: Repurposing Techniques and Methodologies explores the application of computational tools that can be utilized for this approach. The book covers theoretical background and methodologies of chem-bioinformatic techniques and network modeling and discusses the various applied strategies to systematically retrieve, integrate and analyze datasets from diverse sources. Other topics include in silico drug design methods, computational workflows for drug repurposing, and network-based in silico screening for drug efficacy. With contributions from experts in the field and the inclusion of practical case studies, this book gives scientists, researchers and R & D professionals in the pharmaceutical industry valuable insights into drug design.