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Author : Adriano D. Andricopulo
Publisher : Frontiers Media SA
Page : 325 pages
File Size : 17,98 MB
Release : 2022-07-27
Category : Science
ISBN : 2889766314
Author : Adriano D. Andricopulo
Publisher : Frontiers Media SA
Page : 415 pages
File Size : 17,32 MB
Release : 2019-02-05
Category :
ISBN : 2889457443
Chemoinformatics is paramount to current drug discovery. Structure- and ligand-based drug design strategies have been used to uncover hidden patterns in large amounts of data, and to disclose the molecular aspects underlying ligand-receptor interactions. This Research Topic aims to share with a broad audience the most recent trends in the use of chemoinformatics in drug design. To that end, experts in all areas of drug discovery have made their knowledge available through a series of articles that report state-of-the-art approaches. Readers are provided with outstanding contributions focusing on a wide variety of topics which will be of great value to those interested in the many different and exciting facets of drug design.
Author : Kenneth M. Merz
Publisher : Cambridge University Press
Page : 289 pages
File Size : 43,64 MB
Release : 2010-05-31
Category : Medical
ISBN : 0521887232
This book provides a complete snapshot of various experimental approaches to structure-based and ligand-based drug design and is illustrated with more than 200 images.
Author : Mithun Rudrapal
Publisher : Elsevier
Page : 324 pages
File Size : 42,5 MB
Release : 2022-05-26
Category : Science
ISBN : 0323914330
Computer-Aided Drug Design (CADD): From Ligand-Based Methods to Structure-Based Approaches outlines the basic theoretical principles, methodologies and applications of different fundamental and advanced CADD approaches and techniques. Including information on current protocols as well as recent developments in the computational methods, tools and techniques used for rational drug design, the book explains the fundamental aspects of CADD, combining this with a practical understanding of the various in silico approaches used in modern drug discovery processes to assess the field in a comprehensive and systematic manner. Providing up-to-date, information and guidance for scientists, researchers, students and teachers, the book helps readers address specific academic and research related problems using illustrative explanations, examples and case studies, which are systematically reviewed. Highlights in silico approaches to drug design and discovery using computational tools and techniques Details ligand-based and structure-based drug design in a comprehensive and systematic approach Summarizes recent developments in computational drug design strategy as novel approaches of rational drug designing
Author : Tudor I. Oprea
Publisher : John Wiley & Sons
Page : 515 pages
File Size : 13,3 MB
Release : 2006-03-06
Category : Science
ISBN : 3527604200
This handbook provides the first-ever inside view of today's integrated approach to rational drug design. Chemoinformatics experts from large pharmaceutical companies, as well as from chemoinformatics service providers and from academia demonstrate what can be achieved today by harnessing the power of computational methods for the drug discovery process. With the user rather than the developer of chemoinformatics software in mind, this book describes the successful application of computational tools to real-life problems and presents solution strategies to commonly encountered problems. It shows how almost every step of the drug discovery pipeline can be optimized and accelerated by using chemoinformatics tools -- from the management of compound databases to targeted combinatorial synthesis, virtual screening and efficient hit-to-lead transition. An invaluable resource for drug developers and medicinal chemists in academia and industry.
Author : Alexandre Varnek
Publisher : Royal Society of Chemistry
Page : 356 pages
File Size : 38,71 MB
Release : 2008
Category : Computers
ISBN : 0854041443
Chemoinformatics is broadly a scientific discipline encompassing the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information. It is distinct from other computational molecular modeling approaches in that it uses unique representations of chemical structures in the form of multiple chemical descriptors; has its own metrics for defining similarity and diversity of chemical compound libraries; and applies a wide array of statistical, data mining and machine learning techniques to very large collections of chemical compounds in order to establish robust relationships between chemical structure and its physical or biological properties. Chemoinformatics addresses a broad range of problems in chemistry and biology; however, the most commonly known applications of chemoinformatics approaches have been arguably in the area of drug discovery where chemoinformatics tools have played a central role in the analysis and interpretation of structure-property data collected by the means of modern high throughput screening. Early stages in modern drug discovery often involved screening small molecules for their effects on a selected protein target or a model of a biological pathway. In the past fifteen years, innovative technologies that enable rapid synthesis and high throughput screening of large libraries of compounds have been adopted in almost all major pharmaceutical and biotech companies. As a result, there has been a huge increase in the number of compounds available on a routine basis to quickly screen for novel drug candidates against new targets/pathways. In contrast, such technologies have rarely become available to the academic research community, thus limiting its ability to conduct large scale chemical genetics or chemical genomics research. However, the landscape of publicly available experimental data collection methods for chemoinformatics has changed dramatically in very recent years. The term "virtual screening" is commonly associated with methodologies that rely on the explicit knowledge of three-dimensional structure of the target protein to identify potential bioactive compounds. Traditional docking protocols and scoring functions rely on explicitly defined three dimensional coordinates and standard definitions of atom types of both receptors and ligands. Albeit reasonably accurate in many cases, conventional structure based virtual screening approaches are relatively computationally inefficient, which has precluded them from screening really large compound collections. Significant progress has been achieved over many years of research in developing many structure based virtual screening approaches. This book is the first monograph that summarizes innovative applications of efficient chemoinformatics approaches towards the goal of screening large chemical libraries. The focus on virtual screening expands chemoinformatics beyond its traditional boundaries as a synthetic and data-analytical area of research towards its recognition as a predictive and decision support scientific discipline. The approaches discussed by the contributors to the monograph rely on chemoinformatics concepts such as: -representation of molecules using multiple descriptors of chemical structures -advanced chemical similarity calculations in multidimensional descriptor spaces -the use of advanced machine learning and data mining approaches for building quantitative and predictive structure activity models -the use of chemoinformatics methodologies for the analysis of drug-likeness and property prediction -the emerging trend on combining chemoinformatics and bioinformatics concepts in structure based drug discovery The chapters of the book are organized in a logical flow that a typical chemoinformatics project would follow - from structure representation and comparison to data analysis and model building to applications of structure-property relationship models for hit identification and chemical library design. It opens with the overview of modern methods of compounds library design, followed by a chapter devoted to molecular similarity analysis. Four sections describe virtual screening based on the using of molecular fragments, 2D pharmacophores and 3D pharmacophores. Application of fuzzy pharmacophores for libraries design is the subject of the next chapter followed by a chapter dealing with QSAR studies based on local molecular parameters. Probabilistic approaches based on 2D descriptors in assessment of biological activities are also described with an overview of the modern methods and software for ADME prediction. The book ends with a chapter describing the new approach of coding the receptor binding sites and their respective ligands in multidimensional chemical descriptor space that affords an interesting and efficient alternative to traditional docking and screening techniques. Ligand-based approaches, which are in the focus of this work, are more computationally efficient compared to structure-based virtual screening and there are very few books related to modern developments in this field. The focus on extending the experiences accumulated in traditional areas of chemoinformatics research such as Quantitative Structure Activity Relationships (QSAR) or chemical similarity searching towards virtual screening make the theme of this monograph essential reading for researchers in the area of computer-aided drug discovery. However, due to its generic data-analytical focus there will be a growing application of chemoinformatics approaches in multiple areas of chemical and biological research such as synthesis planning, nanotechnology, proteomics, physical and analytical chemistry and chemical genomics.
Author : R. E. Hubbard
Publisher : Royal Society of Chemistry
Page : 279 pages
File Size : 30,97 MB
Release : 2006
Category : Medical
ISBN : 0854043519
Structure-based drug discovery is a collection of methods that exploits the ability to determine and analyse the three dimensional structure of biological molecules. These methods have been adopted and enhanced to improve the speed and quality of discovery of new drug candidates. After an introductory overview of the principles and application of structure-based methods in drug discovery, this book then describes the essential features of the various methods. Chapters on X-ray crystallography, NMR spectroscopy, and computational chemistry and molecular modelling describe how these particular techniques have been enhanced to support rational drug discovery, with discussions on developments such as high throughput structure determination, probing protein-ligand interactions by NMR spectroscopy, virtual screening and fragment-based drug discovery. The concluding chapters complement the overview of methods by presenting case histories to demonstrate the major impact that structure-based methods have had on discovering drug molecules. Written by international experts from industry and academia, this comprehensive introduction to the methods and practice of structure-based drug discovery not only illustrates leading-edge science but also provides the scientific background for the non-expert reader. The book provides a balanced appraisal of what structure-based methods can and cannot contribute to drug discovery. It will appeal to industrial and academic researchers in pharmaceutical sciences, medicinal chemistry and chemical biology, as well as providing an insight into the field for recent graduates in the biomolecular sciences.
Author : Kenneth M. Merz, Jr
Publisher : Cambridge University Press
Page : 289 pages
File Size : 28,86 MB
Release : 2010-05-31
Category : Medical
ISBN : 1107717752
Structure-based (SBDD) and ligand-based (LBDD) drug design are extremely important and active areas of research in both the academic and commercial realms. This book provides a complete snapshot of the field of computer-aided drug design and associated experimental approaches. Topics covered include X-ray crystallography, NMR, fragment-based drug design, free energy methods, docking and scoring, linear-scaling quantum calculations, QSAR, pharmacophore methods, computational ADME-Tox, and drug discovery case studies. A variety of authors from academic and commercial institutions all over the world have contributed to this book, which is illustrated with more than 200 images. This is the only book to cover the subject of structure and ligand-based drug design, and it provides the most up-to-date information on a wide range of topics for the practising computational chemist, medicinal chemist, or structural biologist. Professor Kenneth Merz has been selected as the recipient of the 2010 ACS Award for Computers in Chemical & Pharmaceutical Research that recognizes the advances he has made in the use of quantum mechanics to solve biological and drug discovery problems.
Author : Kunal Roy
Publisher : Elsevier
Page : 768 pages
File Size : 44,64 MB
Release : 2023-05-23
Category : Medical
ISBN : 0443186391
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
Author : Dev Bukhsh Singh
Publisher : Springer Nature
Page : 308 pages
File Size : 11,23 MB
Release : 2020-10-09
Category : Medical
ISBN : 9811568154
This book provides up-to-date information on bioinformatics tools for the discovery and development of new drug molecules. It discusses a range of computational applications, including three-dimensional modeling of protein structures, protein-ligand docking, and molecular dynamics simulation of protein-ligand complexes for identifying desirable drug candidates. It also explores computational approaches for identifying potential drug targets and for pharmacophore modeling. Moreover, it presents structure- and ligand-based drug design tools to optimize known drugs and guide the design of new molecules. The book also describes methods for identifying small-molecule binding pockets in proteins, and summarizes the databases used to explore the essential properties of drugs, drug-like small molecules and their targets. In addition, the book highlights various tools to predict the absorption, distribution, metabolism, excretion (ADME) and toxicity (T) of potential drug candidates. Lastly, it reviews in silico tools that can facilitate vaccine design and discusses their limitations.