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Multi-Scale Approaches in Drug Discovery

Author: Alejandro Speck-Planche

Publisher: Elsevier

Published: 2017-03-07

Total Pages: 0

ISBN-13: 9780081011294

Drug discovery is an expensive, time-consuming process and the modern drug discovery community is constantly challenged not only with discovering novel bioactive agents to combat resistance from known diseases and fight against new ones, but to do so in a way that is economically effective. Advances in both experimental and theoretical/computational methods envisage that the greatest challenges in drug discovery can be most successfully addressed by using a multi-scale approach, drawing on the specialties of a whole host of different disciplines. Multi-Scale Approaches to Drug Discovery furnishes chemists with the detail they need to identify drug leads with the highest potential before isolating and synthesizing them to produce effective drugs with greater swiftness than classical methods may allow. This significantly speeds up the search for more efficient therapeutic agents. After an introduction to multi-scale approaches outlining the need for and benefits of their use, the book goes on to explore a range of useful techniques and research areas, and their potential applications to this process. Profiling drug binding by thermodynamics, machine learning for predicting enzyme sub-classes, and multitasking models for computer-aided design and virtual compound screening are discussed, before the book goes on to review Alkaloid Menispermaceae leads, natural chemotherapeutic agents and methods for speeding up the design and virtual screening of therapeutic peptides. Flavonoids as multi-target compounds are then explored, before the book concludes with a review of Quasi-SMILES as a novel tool. Collecting together reviews and original research contributions written by leading experts in the field, Multi-Scale Approaches to Drug Discovery highlights cutting-edge approaches and practical examples of their implementation for those involved in the drug discovery process at many different levels. Using the combined knowledge of medicinal, computational, pharmaceutical and bio- chemists, it aims to support growth in the multi-scale approach to promote greater success in the development of new drugs.

Multi-Scale Approaches in Drug Discovery

Author: Alejandro Speck-Planche

Publisher: Elsevier

Published: 2017-02-14

Total Pages: 238

ISBN-13: 008101242X

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Semantic Breakthrough in Drug Discovery

Author: Bin Chen

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 10

ISBN-13: 3031794567

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The current drug development paradigm---sometimes expressed as, ``One disease, one target, one drug''---is under question, as relatively few drugs have reached the market in the last two decades. Meanwhile, the research focus of drug discovery is being placed on the study of drug action on biological systems as a whole, rather than on individual components of such systems. The vast amount of biological information about genes and proteins and their modulation by small molecules is pushing drug discovery to its next critical steps, involving the integration of chemical knowledge with these biological databases. Systematic integration of these heterogeneous datasets and the provision of algorithms to mine the integrated datasets would enable investigation of the complex mechanisms of drug action; however, traditional approaches face challenges in the representation and integration of multi-scale datasets, and in the discovery of underlying knowledge in the integrated datasets. The Semantic Web, envisioned to enable machines to understand and respond to complex human requests and to retrieve relevant, yet distributed, data, has the potential to trigger system-level chemical-biological innovations. Chem2Bio2RDF is presented as an example of utilizing Semantic Web technologies to enable intelligent analyses for drug discovery.Table of Contents: Introduction / Data Representation and Integration Using RDF / Data Representation and Integration Using OWL / Finding Complex Biological Relationships in PubMed Articles using Bio-LDA / Integrated Semantic Approach for Systems Chemical Biology Knowledge Discovery / Semantic Link Association Prediction / Conclusions / References / Authors' Biographies

Fragment-based Approaches in Drug Discovery

Author: Wolfgang Jahnke

Publisher: John Wiley & Sons

Published: 2006-12-13

Total Pages: 391

ISBN-13: 3527608605

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This first systematic summary of the impact of fragment-based approaches on the drug development process provides essential information that was previously unavailable. Adopting a practice-oriented approach, this represents a book by professionals for professionals, tailor-made for drug developers in the pharma and biotech sector who need to keep up-to-date on the latest technologies and strategies in pharmaceutical ligand design. The book is clearly divided into three sections on ligand design, spectroscopic techniques, and screening and drug discovery, backed by numerous case studies.

Drug Discovery and Development

Author: Ramarao Poduri

Publisher: Springer Nature

Published: 2021-02-15

Total Pages: 522

ISBN-13: 9811555346

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This book describes the processes that are involved in the development of new drugs. The authors discuss the history, role of natural products and concept of receptor interactions with regard to the initial stages of drug discovery. In a single, highly readable volume, it outlines the basics of pharmacological screening, drug target identification, and genetics involved in early drug discovery. The final chapters introduce readers to stem therapeutics, pharmacokinetics, pharmacovigilance, and toxicological testing. Given its scope, the book will enable research scholars, professionals and young scientists to understand the key fundamentals of drug discovery, including stereochemistry, pharmacokinetics, clinical trials, statistics and toxicology.

Contemporary Chemical Approaches for Green and Sustainable Drugs

Author: Marianna Torok

Publisher: Elsevier

Published: 2022-08-26

Total Pages: 568

ISBN-13: 0128232714

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Contemporary Chemical Approaches for Green and Sustainable Drugs provides readers with the knowledge they need to integrate sustainable approaches into their work. Sections cover different aspects of green and sustainable drug development from design to disposal, including computer-aided drug design, green resourcing of drugs and drug candidates, an overview of the health concerns of pharmaceutical pollution, and a survey of potential chemical methods for its reduction. Drawing together the knowledge of a global team of experts, this book provides an inclusive overview of the chemical tools and approaches available for minimizing the negative environmental impact of current and newly developed drugs. This will be a useful guide for all academic and industrial researchers across green and sustainable chemistry, medicinal chemistry, environmental chemistry and pharmaceutical science. Provides an integrative overview of the environmental risks of drugs and drug by products to support chemists in pre-emptively addressing these issues Highlights the advantages of computer-aided drug design, green and sustainable sourcing, and novel methods for the production of safer, more effective drugs Presents individual chapters written by renowned experts with diverse backgrounds Reflects research in practice through selected case studies and extensive state-of-the-art reference sections to serve as a starting point in the design of any specialized environmentally-conscious medicinal chemistry project

Multiscale Modeling for Process Safety Applications

Author: Arnab Chakrabarty

Publisher: Butterworth-Heinemann

Published: 2015-11-29

Total Pages: 446

ISBN-13: 0123972833

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Multiscale Modeling for Process Safety Applications is a new reference demonstrating the implementation of multiscale modeling techniques on process safety applications. It is a valuable resource for readers interested in theoretical simulations and/or computer simulations of hazardous scenarios. As multi-scale modeling is a computational technique for solving problems involving multiple scales, such as how a flammable vapor cloud might behave if ignited, this book provides information on the fundamental topics of toxic, fire, and air explosion modeling, as well as modeling jet and pool fires using computational fluid dynamics. The book goes on to cover nanomaterial toxicity, QPSR analysis on relation of chemical structure to flash point, molecular structure and burning velocity, first principle studies of reactive chemicals, water and air reactive chemicals, and dust explosions. Chemical and process safety professionals, as well as faculty and graduate researchers, will benefit from the detailed coverage provided in this book. Provides the only comprehensive source addressing the use of multiscale modeling in the context of process safety Bridges multiscale modeling with process safety, enabling the reader to understand mapping between problem detail and effective usage of resources Presents an overall picture of addressing safety problems in all levels of modeling and the latest approaches to each in the field Features worked out examples, case studies, and a question bank to aid understanding and involvement for the reader

Coronavirus Drug Discovery

Author: Chukwuebuka Egbuna

Publisher: Elsevier

Published: 2022-05-25

Total Pages: 390

ISBN-13: 0323851789

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Coronavirus Drug Discovery, Volume 1: SARS-CoV-2 (COVID-19) Prevention, Diagnosis, and Treatment is the first of three volumes presenting comprehensive information on drug discovery against COVID-19. This volume provides background information on the genesis of COVID-19, the epidemiology, transmission, pathogenesis, and mutagenesis. It also presents the various treatment options, drug discovery opportunities and vaccine developmental processes. Written by global team of experts from key institutions across the globe, this book is recommended to all concerned agencies, private research firms, and consortiums working on finding a solution to COVID-19 and its variants. By design, this book will be useful to drug developers, medicinal chemists, pharmacologists, health experts, researchers, students and faculty members in industry and academia. Presents information on the genesis of COVID-19, global impact and collaborative efforts Details the epidemiology, transmission, pathogenesis, and mutagenesis of SARS-CoV-2 Documents the various treatment options and vaccine development for COVID-19

Machine Learning in Chemistry

Author: Hugh M. Cartwright

Publisher: Royal Society of Chemistry

Published: 2020-07-15

Total Pages: 564

ISBN-13: 1788017897

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Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field.

Computational Drug Discovery

Author: Vasanthanathan Poongavanam

Publisher: John Wiley & Sons

Published: 2024-01-19

Total Pages: 882

ISBN-13: 3527840737

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Computational Drug Discovery A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery. Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented. To offer the most up-to-date information on computational methods utilized in computational drug discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts. The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery. Key topics covered in the book include: Application of molecular dynamics simulations and related approaches in drug discovery The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening. Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts. Methods for performing ultra-large-scale virtual screening for hit identification. Computational strategies for designing new therapeutic models, including PROTACs and molecular glues. In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints. The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery This book will provide readers an overview of the latest advancements in computational drug discovery and serve as a valuable resource for professionals engaged in drug discovery.