Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment

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Language: English
Cover of the book Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment

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3D-QSAR; 4D-QSAR; 5D-QSAR; 6D-QSAR; 7D-QSAR; Applicability domain (AD); Approved drug; Artificial neural network (ANN); COMBINE; Chemical assessment; Chemical attributes; Chemical graph theory; Chemical information; Chemometric tools; Classification; Clinical trial; CoRIA; Comparative molecular field analysis (CoMFA); Comparative molecular moment analysis (CoMMA); Comparative molecular similarity indices analysis (CoMSIA); Comparative spectral analysis (CoSA); Computer; Cosmetics QSAR; Database mining; De novo model; Descriptor; Descriptors; Docking; EQSAR; Electronic; Extrathermodynamic approach; G-QSAR; Graph theory; HQSAR; In silico; In silico success; Interspecies toxicity QSAR; LQTA-QSAR; Lead optimization; Linear discriminant analysis (LDA); Linear free-energy-related (LFER) model; MIA-QSAR; Mathematical model; Matrix; Mixed approach; Mixture toxicity QSAR; Model development; Modeling; Molecular dynamics (MD); Molecular mechanics (MM); Molecular shape analysis (MSA); Multiple linear regression (MLR); Nano-QSAR; Organisation for Economic Cooperation and Development (OECD); Partial least squares (PLS); Peptide QSAR; Pharmacophore; Physicochemical; Quantitative structure-activity relationship (QSAR); Quantum; Randomization; Receptor surface analysis (RSA); Regression; Self-organizing molecular field analysis (SOMFA); Semiempirical quantum mechanics; Simulation; Structural; Topological; Topology; Two-dimensional (2D) chemistry; Validation; Virtual screening; Weighted holistic invariant molecular (WHIM)

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484 p. · 19x23.3 cm · Paperback
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.
Chapter 1: Background of QSAR and Historical developmentsChapter 2: Chemical information and DescriptorsChapter 3: Classical QSARChapter 4: Topological QSARChapter 5: Computational chemistryChapter 6: Selected Statistical methods in QSARChapter 7: Validation of QSAR modelsChapter 8: Introduction to 3D-QSARChapter 9: Newer QSAR techniquesChapter 10: Other related techniquesChapter 11: Future avenues
New researchers, professors and graduate students across the pharmaceutical sciences (including pharmacology, toxicology and medicinal chemistry); secondary audience of regulatory officials and risk assessors in toxicology and environmental health
Dr. Kunal Roy is a Professor and Ex-Head in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and Marie Curie International Incoming Fellowship (University of Manchester, 2013). The field of his research interest is QSAR and Molecular Modeling with application in Drug Design and Ecotoxicological Modeling. Dr. Roy has published more than 350 research articles in refereed journals (current SCOPUS h index 49). He has also coauthored two QSAR-related books, edited six QSAR books and published more than ten book chapters. Dr. Roy is a Co-Editor-in-Chief of Molecular Diversity (Springer Nature). He also serves as a member of the Editorial Boards of several International Journals.
  • 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