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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
Covering computational tools in drug design using techniques from chemoinformatics, molecular modelling and computational chemistry, this book explores these methodologies and applications of in silico medicinal chemistry. The first part of the book covers molecular representation methods in computing in terms of chemical structure, together with guides on common structure file formats. The second part examines commonly used classes of molecular descriptors. The third part provides a guide to statistical learning methods using chemical structure data, covering topics such as similarity searching, clustering and diversity selection, virtual library design, ligand docking and de novo design. The final part of the book summarises the application of methods to the different stages of drug discovery, from target ID, through hit finding and hit-to-lead, to lead optimisation. This book is a practical introduction to the subject for researchers new to the fields of chemoinformatics, molecular modelling and computational chemistry.
Learn how AI and data science are upending the worlds of biology and medicine In Silico Dreams: How Artificial Intelligence and Biotechnology Will Create the Medicines of the Future delivers an illuminating and fresh perspective on the convergence of two powerful technologies: AI and biotech. Accomplished genomics expert, executive, and author Brian Hilbush offers readers a brilliant exploration of the most current work of pioneering tech giants and biotechnology startups who have already started disrupting healthcare. The book provides an in-depth understanding of the sources of innovation that are driving the shift in the pharmaceutical industry away from serendipitous therapeutic discovery and toward engineered medicines and curative therapies. In this fascinating book, you'll discover: An overview of the rise of data science methods and the paradigm shift in biology that led to the in silico revolution An outline of the fundamental breakthroughs in AI and deep learning and their applications across medicine A compelling argument for the notion that AI and biotechnology tools will rapidly accelerate the development of therapeutics A summary of innovative breakthroughs in biotechnology with a focus on gene editing and cell reprogramming technologies for therapeutic development A guide to the startup landscape in AI in medicine, revealing where investments are poised to shape the innovation base for the pharmaceutical industry Perfect for anyone with an interest in scientific topics and technology, In Silico Dreams also belongs on the bookshelves of decision-makers in a wide range of industries, including healthcare, technology, venture capital, and government.
Photosynthesis in silico: Understanding Complexity from Molecules to Ecosystems is a unique book that aims to show an integrated approach to the understanding of photosynthesis processes. In this volume - using mathematical modeling - processes are described from the biophysics of the interaction of light with pigment systems to the mutual interaction of individual plants and other organisms in canopies and large ecosystems, up to the global ecosystem issues. Chapters are written by 44 international authorities from 15 countries. Mathematics is a powerful tool for quantitative analysis. Properly programmed, contemporary computers are able to mimic complicated processes in living cells, leaves, canopies and ecosystems. These simulations - mathematical models - help us predict the photosynthetic responses of modeled systems under various combinations of environmental conditions, potentially occurring in nature, e.g., the responses of plant canopies to globally increasing temperature and atmospheric CO2 concentration. Tremendous analytical power is needed to understand nature's infinite complexity at every level.
Countless medical researchers over the past century have been occupied by the search for a cure of cancer. So far, they have developed and implemented a wide range of treatment techniques, including surgery, chemo- and radiotherapy, antiangiogenic drugs, small molecule inhibitors, and oncolytic viruses. However, patterns of these treatments' effectiveness remain largely unclear, and a better understanding of how cancer therapies work has become a key research goal. Cancer Treatment in Silico provides the first in-depth study of approaching this understanding by modeling cancer treatments, both mathematically and through computer simulations. The main goal of this book is to help expose students and researchers to in silico methods of studying cancer. It is intended for both the applied mathematics and experimental oncology communities, as mathematical models are playing an increasingly important role to supplement laboratory biology in the fight against cancer. Written at a level that generally requires little technical background, the work will be a valuable resource for scientists and students alike.
This fully updated book explores all-new and revised protocols involving the use of in silico models, particularly with regard to pharmaceuticals. Divided into five sections, the volume covers the modeling of pharmaceuticals in the body, toxicity data for modeling purposes, in silico models for multiple endpoints, a number of platforms for evaluating pharmaceuticals, as well as an exploration of challenges, both scientific and sociological. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice necessary for successful results. Authoritative and comprehensive, In Silico Methods for Predicting Drug Toxicity, Second Edition aims to guide the reader through the correct procedures needed to harness in silico models, a field which now touches a wide variety of research specialties.
In Silico Drug Discovery and Design: Theory, Methods, Challenges, and Applications provides a comprehensive, unified, and in-depth overview of the current methodological strategies in computer-aided drug discovery and design. Its main aims are to introduce the theoretical framework and algorithms, discuss the range of validity, strengths and limitations of each methodology, and present applications to real world problems in the drug discovery arena. Special emphasis has been given to the emerging and most pressing methodological challenges in in silico drug discovery and design. The book assumes a basic knowledge of physical principles and molecular modeling. Particular attention has been paid to outline the underlying physico-chemical foundation of the methods described, thus providing the necessary background to avoid a -black-box- approach. In each self-contained chapter, this is presented together with the latest developments and applications, and the challenges that lie ahead. Assembling a unique team of experts to weigh in on the most important issues influencing modern computational drug discovery and design, this book constitutes both a desktop reference to academic and industrial researchers in the field, and a textbook for students in the area of molecular modeling and drug discovery. Comprised of 18 chapters and divided into three parts, this book: Provides a comprehensive, unified, and in-depth overview of the current methodological strategies in computer-aided drug discovery and design Outlines the underlying physico-chemical foundation of the methods described Presents several applications of computational methods to real world problems in the drug design field Helps to avoid a -black-box- approach to in silico drug discovery Constitutes an actual textbook for students in the area of molecular modeling and drug discovery Gives the reader the adequate background to face the current challenges of the field In Silico Drug Discovery and Design: Theory, Methods, Challenges, and Applications describes the theoretical framework, methods, practical applications and case examples relevant to computer-aided drug lead discovery and design. This text will surely aid in understanding the underlying physical foundation of computational tools and their range of application, thus facilitating the interpretation of simulation results.
Systems biology can now be considered an established and fundamental field in life sciences. It has moved from the identification of molecular 'parts lists' for living organisms towards synthesising information from different 'omics'-based approaches to generate and test new hypotheses about how biological systems work. In In Silico Systems Biology: Methods and Protocols, expert researchers in the field detail a practical set of chapters based often on actual materials used and develop for face-to-face training with examples and case studies. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step workflows, and key tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, In Silico Systems Biology: Methods and Protocols seeks to aid scientists in the further study of network biology and mathematical models of biological systems.
Bees are critically important for ecosystem function and biodiversity maintenance through their pollinating activity. This book provides a collection of computational methods to those primarily interested in the study of the ecology, ethology, and ecotoxicology of bees. It presents numerous case studies to enable readers to understand the appropriateness but also the limitations of models in theoretical and applied bee research. Written by an international team of experts, this book covers the main types of modeling approaches that can be used in terrestrial ecology and applied ecotoxicology.
The pharmaceutical industry relies on numerous well-designed experiments involving high-throughput techniques and in silico approaches to analyze potential drug targets. These in silico methods are often predictive, yielding faster and less expensive analyses than traditional in vivo or in vitro procedures. In Silico Technologies in Drug Target Ide