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Concepts and Experimental Protocols of Modelling and Informatics in Drug Design discusses each experimental protocol utilized in the field of bioinformatics, focusing especially on computer modeling for drug development. It helps the user in understanding the field of computer-aided molecular modeling (CAMM) by presenting solved exercises and examples. The book discusses topics such as fundamentals of molecular modeling, QSAR model generation, protein databases and how to use them to select and analyze protein structure, and pharmacophore modeling for drug targets. Additionally, it discusses data retrieval system, molecular surfaces, and freeware and online servers. The book is a valuable source for graduate students and researchers on bioinformatics, molecular modeling, biotechnology and several members of biomedical field who need to understand more about computer-aided molecular modeling. - Presents exercises with solutions to aid readers in validating their own protocol - Brings a thorough interpretation of results of each exercise to help readers compare them to their own study - Explains each parameter utilized in the algorithms to help readers understand and manipulate various features of molecules and target protein to design their study
Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability. The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others. This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients. - Discusses in silico remodeling of effective phytochemical compounds for discovering improved anticancer agents for substantial/significant cancer therapy - Covers potential tools of bioinformatics that are applied toward discovering new targets by drug repurposing and strategies to cure different types of cancers - Demonstrates the significance of computational and artificial intelligence approaches in anticancer drug discovery - Explores how these various advances can be integrated into a precision and personalized medicine approach that can eventually enhance patient care
This book overviews purinergic receptors that are playing key roles in human and pathophysiological processes. The book elaborates on how selective P1 and P2 modulators have been developed as therapeutics for a variety of diseases. It also provides an overview of current perspectives in the design of purinergic receptor modulators and future challenges such as the availability of selective ligands for all receptor subtypes. Divided into 12 chapters, this comprehensive volume also offers a multidisciplinary perspective on the historical evolution, starting with a chapter devoted to the roots and early discoveries of adenosine and its receptors, followed by a twenty-year retrospective on the synthesis, properties, and functional potential of adenosine receptor ligands, probes, and functional conjugates. In the next chapters, experts in the field delve into topics such as the therapeutic potential of adenosine receptor ligands in wound healing and fibrosis, the therapeutic benefits of A2A receptor antagonists, the A2B adenosine receptor as a target for brain ischemia or demyelination, the development and latest advancements in clinical trials of A3 adenosine receptor ligands. Other chapters describe bifunctional tools to study adenosine receptors, allosteric modulators of adenosine receptors, and new computational approaches to inspect adenosine receptor-ligand recognition processes. Furthermore, the book discusses the role of P2X4 receptors in immunity and inflammation. The final chapters illustrate CD73 inhibitors as antitumor agents, and bacterial ectonucleotidases as underexplored antibacterial drug targets. This book is a valuable resource for scholars working in the field of medicinal chemistry, as well as researchers in the industry, providing readers with a comprehensive understanding of adenosine receptor biology and its therapeutic potential.
The book is devoted to an important aspect of pharmacology and pharmaceutical chemistry, i.e. the significance of stereoisomerism of drugs for their biological effect from the point of view of their pharmacokinetics, pharmacodynamics and toxicology. The authors review the landmarks in the development of stereochemistry and stereopharmacology. Present-day IUPAC terminology is discussed; general issues of stereoisomerism are considered including separation of racemic mixtures and asymmetric synthesis of isomers, methods of quantifying the isomers of a drug in biological material. The authors put special emphasis on general problems of the influence of stereoisomerism on pharmacological and adverse effects of drugs. A classification of drugs based on stereochemical properties of their isomers is proposed. Possibilities of interaction of stereoisomers in racemic mixtures are discussed. A considerable portion of the book is devoted to pharmacological action of the main groups of drugs whose structure includes asymmetric atoms (that is, drugs with several isomers). Detailed attention is paid to advisability of developing single isomer drugs and to the specifics of their study at the stage of preclinical and clinical trials.
This volume details the development of updated dry lab and wet lab based methods for the reconstruction of Gene regulatory networks (GRN). Chapters guide readers through culprit genes, in-silico drug discovery techniques, genome-wide ChIP-X data, high-Throughput Transcriptomic Data Exome Sequencing, Next-Generation Sequencing, Fuorescence Spectroscopy, data analysis in Bioinformatics, Computational Biology, and S-system based modeling of GRN. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Reverse Engineering of Regulatory Networks aims to be a useful and practical guide to new researchers and experts looking to expand their knowledge.
Dosage Forms, Formulation Developments and Regulations, Volume One in the Recent and Future Trends in Pharmaceutics series, explores aspects of pharmaceutics, with an original approach focused on technology, novelties and future trends in the field. The book discusses the most recent developments in pharmaceutical preformulation and formulation studies, biopharmaceutics and novel pharmaceutical formulations, regulatory affairs, and good manufacturing practices. Exciting areas such as formulation strategies, optimization techniques, the biopharmaceutical classification system, and pharmaceutical aerosols are included. The field of pharmaceutics is highly dynamic and rapidly expanding day-by-day, so it demands a variety of amplified efforts for designing and developing pharmaceutical processes and formulation strategies. This is an essential reference for researchers in academia and industry as well as advanced graduate students in pharmaceutics. - Examines trends and recent technologies in dosage, formulation and regulation - Contains contributions from leading experts in academia, research, industry and regulatory agencies - Includes high-quality illustrations, flow charts and tables for easy understanding of concepts - Discusses practical examples and research case studies
Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences brings together two very important fields in pharmaceutical sciences that have been mostly seen as diverging from each other: chemoinformatics and bioinformatics. As developing drugs is an expensive and lengthy process, technology can improve the cost, efficiency and speed at which new drugs can be discovered and tested. This book presents some of the growing advancements of technology in the field of drug development and how the computational approaches explained here can reduce the financial and experimental burden of the drug discovery process. This book will be useful to pharmaceutical science researchers and students who need basic knowledge of computational techniques relevant to their projects. Bioscientists, bioinformaticians, computational scientists, and other stakeholders from industry and academia will also find this book helpful. - Provides practical information on how to choose and use appropriate computational tools - Presents the wide, intersecting fields of chemo-bio-informatics in an easily-accessible format - Explores the fundamentals of the emerging field of chemoinformatics and bioinformatics
Helps you choose the right computational tools and techniques to meet your drug design goals Computational Drug Design covers all of the major computational drug design techniques in use today, focusing on the process that pharmaceutical chemists employ to design a new drug molecule. The discussions of which computational tools to use and when and how to use them are all based on typical pharmaceutical industry drug design processes. Following an introduction, the book is divided into three parts: Part One, The Drug Design Process, sets forth a variety of design processes suitable for a number of different drug development scenarios and drug targets. The author demonstrates how computational techniques are typically used during the design process, helping readers choose the best computational tools to meet their goals. Part Two, Computational Tools and Techniques, offers a series of chapters, each one dedicated to a single computational technique. Readers discover the strengths and weaknesses of each technique. Moreover, the book tabulates comparative accuracy studies, giving readers an unbiased comparison of all the available techniques. Part Three, Related Topics, addresses new, emerging, and complementary technologies, including bioinformatics, simulations at the cellular and organ level, synthesis route prediction, proteomics, and prodrug approaches. The book's accompanying CD-ROM, a special feature, offers graphics of the molecular structures and dynamic reactions discussed in the book as well as demos from computational drug design software companies. Computational Drug Design is ideal for both students and professionals in drug design, helping them choose and take full advantage of the best computational tools available. Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.
Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more.
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. - Presents chemometrics, cheminformatics and machine learning methods under a single reference - Showcases the different structure-based, ligand-based and machine learning tools currently used in drug design - Highlights special topics of computational drug design and available tools and databases