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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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Pure sciences. Natural sciences (general)

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Pure sciences. Natural sciences (general)

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Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment describes the historical evolution of quantitative structure-activity relationship (QSAR) approaches and their fundamental principles.  This book includes clear, introductory coverage of the statistical methods applied in QSAR and new QSAR techniques, such as HQSAR and G-QSAR.  Containing real-world examples that illustrate important methodologies, this book identifies QSAR as a valuable tool for many different applications, including drug discovery, predictive toxicology and risk assessment.  Written in a straightforward and engaging manner, this is the ideal resource for all those looking for general and practical knowledge of QSAR methods. Includes numerous practical examples related to QSAR methods and applications Follows the Organization for Economic Co-operation and Development principles for QSAR model development Discusses related techniques such as structure-based design and the combination of structure- and ligand-based design tools.

MEDICAL --- Pharmacology --- QSAR (Biochemistry) --- Health & Biological Sciences --- Human Anatomy & Physiology --- Pharmacy, Therapeutics, & Pharmacology --- Animal Biochemistry --- Structure-activity relationships (Biochemistry) --- Biochemorphology --- Biomolecules --- Chemical structure-biological activity relationships --- Relationships, Structure-activity (Biochemistry) --- Physical biochemistry --- Quantitative structure-activity relationships (Biochemistry) --- Structure-activity relationships