Molecular Docking For Computer Aided Drug Design

Molecular Docking for Computer-Aided Drug Design: Fundamentals, Techniques, Resources and Applications offers in-depth coverage on the use of molecular docking for drug design. The book is divided into three main sections that cover basic techniques, tools, web servers and applications. It is an essential reference for students and researchers involved in drug design and discovery. Covers the latest information and state-of-the-art trends in structure-based drug design methodologies Includes case studies that complement learning Consolidates fundamental concepts and current practice of molecular docking into one convenient resource

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.

Bioinformatics allows researchers to answer biological questions with advanced computational methods which involves the application of statistics and mathematical modeling. Structural bioinformatics enables the prediction and analysis of 3D structures of macromolecules while Computer Aided Drug Designing (CADD) assists scientists to design effective active molecules against diseases. However, the concepts in structural bioinformatics and CADD can be complex to understand for students and educated laymen. This quick guideline is intended as a basic manual for beginner students and instructors involved in bioinformatics and computational chemistry courses. Readers will learn the basics of structural bioinformatics, primary and secondary analysis and prediction, structural visualization, structural analysis and molecular docking. Therefore, the book is a useful handbook for aspiring scholars who wish to learn the basic concepts in computational analysis of biomolecules.

The role of technology in the medical field has resulted in significant developments within the pharmaceutical industry. Computational approaches have emerged as a crucial method in further advancing drug design and development. Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery presents emerging research on the application of computer-assisted design methods for drugs, emphasizing the benefits and improvements that molecular docking has caused within the pharmaceutical industry. Focusing on validation methods, search algorithms, and scoring functions, this book is a pivotal resource for professionals, researchers, students, and practitioners in the field of theoretical and computational chemistry.

As the pharmaceutical industry continues to advance, new techniques in drug design are emerging. In order to deliver optimum care to patients, the development of innovative pharmacological techniques has become a widely studied topic. Applied Case Studies and Solutions in Molecular Docking-Based Drug Design is a pivotal reference source for the latest scholarly research on the progress of pharmaceutical design and computational approaches in the field of molecular docking. Highlighting innovative research perspectives and real-world applications, this book is ideally designed for professionals, researchers, practitioners, and medical chemists actively involved in computational chemistry and pharmaceutical sciences.

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

The Ernst Schering Research Foundation sponsored its 15th workshop in Berlin on October 19-21, 1994. Leading scientists from Europe and North America were invited to discuss computer-aided drug design in industrial research. Computer-aided drug design is a very exciting field and an intellec tual challenge, like playing chess. But these reasons are no longer suf ficient to justify using this method in industry, if they ever were. Fig. 1. The participants of the workshop VI Preface Therefore, when we, together with Prof. Hoyer, started to think about this workshop, our intentions quickly became clear. We were not so much interested in the very latest developments of methods or in computer-aided drug design itself - enough conferences have dealt with these topics. However, we were very interested in the usefulness and limitations of computer-aided drug design in the indu strial research process. A lot has changed in the pharmaceutical industry recently. These changes are gaining momentum, so it is the right time to think about the role of computer-aided drug design in this changing environment.

This book presents various computer-aided drug discovery methods for the design and development of ligand and structure-based drug molecules. A wide variety of computational approaches are now being used in various stages of drug discovery and development, as well as in clinical studies. Yet, despite the rapid advances in computer software and hardware, combined with the exponential growth in the available biological information, there are many challenges that still need to be addressed, as this book shows. In turn, it shares valuable insights into receptor-ligand interactions in connection with various biological functions and human diseases. The book discusses a wide range of phylogenetic methods and highlights the applications of Molecular Dynamics Simulation in the drug discovery process. It also explores the application of quantum mechanics in order to provide better accuracy when calculating protein-ligand binding interactions and predicting binding affinities. In closing, the book provides illustrative descriptions of major challenges associated with computer-aided drug discovery for the development of therapeutic drugs. Given its scope, it offers a valuable asset for life sciences researchers, medicinal chemists and bioinformaticians looking for the latest information on computer-aided methodologies for drug development, together with their applications in drug discovery.

This book, aimed at academics, industrialists and post-graduates, documents the latest research into computer aided drug design.

Frontiers in Computational Chemistry presents contemporary research on molecular modeling techniques used in drug discovery and the drug development process: computer aided molecular design, drug discovery and development, lead generation, lead optimization, database management, computer and molecular graphics, and the development of new computational methods or efficient algorithms for the simulation of chemical phenomena including analyses of biological activity. The third volume of this series features four chapters covering in silico approaches to computer aided drug design, modeling of platinum and adjuvant anti-cancer drugs, allostery in proteins and studies on the theory of chemical space in electron systems.

Computer-aided drug design and in silico screening have contributed to the discovery of several compounds that have either reached the market or entered clinical trials. In silico Lead Discovery is a compilation of the efforts of several experts on bioinf

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. Discusses the theoretical background and methodologies of useful techniques of cheminformatics and bioinformatics that can be applied for drug repurposing Offers case studies relating to the in silico modeling of FDA-approved drugs for the discovery of antifungal, anticancer, antiplatelet agents, and for drug therapies against diseases Covers tools and databases that can be utilized to facilitate in silico methods for drug repurposing

This book explains deep learning concepts and derives semi-supervised learning and nuclear learning frameworks based on cognition mechanism and Lie group theory. Lie group machine learning is a theoretical basis for brain intelligence, Neuromorphic learning (NL), advanced machine learning, and advanced artifi cial intelligence. The book further discusses algorithms and applications in tensor learning, spectrum estimation learning, Finsler geometry learning, Homology boundary learning, and prototype theory. With abundant case studies, this book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artifi cial intelligence, machine learning, automation, mathematics, management science, cognitive science, financial management, and data analysis. In addition, this text can be used as the basis for teaching the principles of machine learning. Li Fanzhang is professor at the Soochow University, China. He is director of network security engineering laboratory in Jiangsu Province and is also the director of the Soochow Institute of industrial large data. He published more than 200 papers, 7 academic monographs, and 4 textbooks. Zhang Li is professor at the School of Computer Science and Technology of the Soochow University. She published more than 100 papers in journals and conferences, and holds 23 patents. Zhang Zhao is currently an associate professor at the School of Computer Science and Technology of the Soochow University. He has authored and co-authored more than 60 technical papers.

Zinc-dependent enzymes; HDAC; LpxC; computer-aided drug design; molecular modeling; homology modeling; molecular docking; binding free energy calculations; virtual screening; antiparasitic

Drug discovery is all about finding small molecules that interact in a desired way with larger molecules, namely proteins and other macromolecules in the human body. If the three-dimensional structures of both the small and large molecule are known, their interaction can be tested by computer simulation with a reasonable degree of accuracy. Alternatively, if active ligands are already available, molecular similarity searches can be used to find new molecules. This virtual screening can even be applied to compounds that have yet to be synthesized, as opposed to "real" screening that requires cost- and labor-intensive laboratory testing with previously synthesized drug compounds. Unique in its focus on the end user, this is a real "how to" book that does not presuppose prior experience in virtual screening or a background in computational chemistry. It is both a desktop reference and practical guide to virtual screening applications in drug discovery, offering a comprehensive and up-to-date overview. Clearly divided into four major sections, the first provides a detailed description of the methods required for and applied in virtual screening, while the second discusses the most important challenges in order to improve the impact and success of this technique. The third and fourth, practical parts contain practical guidelines and several case studies covering the most important scenarios for new drug discovery, accompanied by general guidelines for the entire workflow of virtual screening studies. Throughout the text, medicinal chemists from academia, as well as from large and small pharmaceutical companies report on their experience and pass on priceless practical advice on how to make best use of these powerful methods.

Molecular similarity searching is fast becoming a key tool in organic chemistry. In this book, the editor has brought together an international team of authors, each working at the forefront of this technology, providing a timely and concise overview of current research. The chapters focus principally on those methods which have reached sufficient maturity to be of immediate practical use in molecular design.

This book reviews recent physicochemical and biophysical techniques applied in drug discovery research, and it outlines the latest advances in computational drug design. Divided into 10 chapters, the book discusses about the role of structural biology in drug discovery, and offers useful application cases of several biophysical and computational methods, including time-resolved fluorometry (TRF) with Förster resonance energy transfer (FRET), X-Ray crystallography, nuclear magnetic resonance spectroscopy, mass spectroscopy, generative machine learning for inverse molecular design, quantum mechanics/molecular mechanics (QM/MM,ONIOM) and quantum molecular dynamics (QMT) methods. Particular attention is given to computational search techniques applied to peptide vaccines using novel mathematical descriptors and structure and ligand-based virtual screening techniques in drug discovery research. Given its scope, the book is a valuable resource for students, researchers and professionals from pharmaceutical industry interested in drug design and discovery.

Specially designed computer software is revolutionizing procedures for structured or rational drug design and discovery. The Guidebook on Molecular Modeling in Drug Design serves as a manual for the analysis ofmolecular structure and the correlation of these structures with pharmacological reactions. Intended as an introductory guide for advanced students and professionals with an interest in computer-assisted modeling for drug design and discovery, this bookwill also be of interest to medicinal and organic chemists, pharmaceutical researchers, pharmacologists, and biochemists who want to gain further insight into this rapidly advancing field. Molecular modeling is assuming an important role in the understanding of three-dimensional aspects in the specificity of drug-receptor interactions at the molecular level. This research area has become a well-established discipline in pharmaceutical research. It has created unprecedented opportunities in assisting medicinal chemists in the design of new therapeutic agents. Advances made in computer hardware and in theoretical medicinal chemistry have brought high-performance computing and graphics tools within reach of most academic and industrial laboratories, facilitating the development of useful approaches to rational drug design. The Guidebook on Molecular Modeling in Drug Design serves as a manual for the analysis of the molecular structure of biological molecules and drugs and the correlation of these structures with pharmacological actions. Intended as a guide for advanced students and professionals with an interest in computer-assisted modeling for drug design and discovery, this book will also be of interest to medicinal and organic chemists, pharmaceutical researchers, pharmacologists, and biochemists who want to gain further insight into this rapidly advancing field.

This book constitutes the refereed proceedings of the 10th International Conference on E-Learning and Games, Edutainment 2016, held in Hangzhou, China, in April 2016. The 36 full papers presented were carefully reviewed and selected from 60 submissions. They are organized in the following topical sections: E-learning and game; graphics, imaging and applications; intelligent data analytics and visualization.