This book reviews the advances and challenges of structure-based drug design in the preclinical drug discovery process, addressing various diseases, including malaria, tuberculosis and cancer. Written by internationally recognized researchers, this edited book discusses how the application of the various in-silico techniques, such as molecular docking, virtual screening, pharmacophore modeling, molecular dynamics simulations, and residue interaction networks offers insights into pharmacologically active novel molecular entities. It presents a clear concept of the molecular mechanism of different drug targets and explores methods to help understand drug resistance. In addition, it includes chapters dedicated to natural-product- derived medicines, combinatorial drug discovery, the CryoEM technique for structure-based drug design and big data in drug discovery. The book offers an invaluable resource for graduate and postgraduate students, as well as for researchers in academic and industrial laboratories working in the areas of chemoinformatics, medicinal and pharmaceutical chemistry and pharmacoinformatics.

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.

Designing and developing new drugs is an expensive and time-consuming process, and there is a need to discover new tools or approaches that can optimize this process. Applied Computer-Aided Drug Design: Models and Methods compiles information about the main advances in computational tools for discovering new drugs in a simple and accessible language for academic students to early career researchers. The book aims to help readers understand how to discover molecules with therapeutic potential by bringing essential information about the subject into one volume. Key Features · Presents the concepts and evolution of classical techniques, up to the use of modern methods based on computational chemistry in accessible format. · Gives a primer on structure- and ligand-based drug design and their predictive capacity to discover new drugs. · Explains theoretical fundamentals and applications of computer-aided drug design. · Focuses on a range of applications of the computations tools, such as molecular docking; molecular dynamics simulations; homology modeling, pharmacophore modeling, quantitative structure-activity relationships (QSAR), density functional theory (DFT), fragment-based drug design (FBDD), and free energy perturbation (FEP). · Includes scientific reference for advanced readers Readership Students, teachers and early career researchers.

BIOINFORMATICS TOOLS FOR Pharmaceutical DRUG PRODUCT DLEVELOPMENT A timely book that details bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies, for drug development in the pharmaceutical and medical sciences industries. The book contains 17 chapters categorized into 3 sections. The first section presents the latest information on bioinformatics tools, artificial intelligence, machine learning, computational methods, protein interactions, peptide-based drug design, and omics technologies. The following 2 sections include bioinformatics tools for the pharmaceutical sector and the healthcare sector. Bioinformatics brings a new era in research to accelerate drug target and vaccine design development, improving validation approaches as well as facilitating and identifying side effects and predicting drug resistance. As such, this will aid in more successful drug candidates from discovery to clinical trials to the market, and most importantly make it a more cost-effective process overall. Readers will find in this book: Applications of bioinformatics tools for pharmaceutical drug product development like process development, pre-clinical development, clinical development, commercialization of the product, etc.; The ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach; The broad and deep background, as well as updates, on recent advances in both medicine and AI/ML that enable the application of these cutting-edge bioinformatics tools. Audience The book will be used by researchers and scientists in academia and industry including drug developers, computational biochemists, bioinformaticians, immunologists, pharmaceutical and medical sciences, as well as those in artificial intelligence and machine learning.

The process of drug discovery and development is a complex multistage logistics project spanned over 10-15 years with an average budget exceeding 1 billion USD. Starting with target identification and synthesizing anywhere between 10k to 15k synthetic compounds to potentially obtain the final drug that reaches the market involves a complicated maze with multiple inter- and intra-operative fields. Topics described in this book emphasize the progresses in computational applications, pharmacokinetics advances, and molecular modeling developments. In addition the book also contains special topics describing target deorphaning in Mycobacterium tuberculosis, therapy treatment of some rare diseases, and developments in the pediatric drug discovery process.

Computational methods and understanding computational models are important in modern drug discovery. The book focuses on computational approaches that can improve the development of in silico methodologies. It includes lead hit methods, docking algorithms, computational chiral compounds, structure-based drug design, GROMACS and NAMD, structural genomics, toxicity prediction, enzyme inhibitors and peptidomimetic therapeutics

This contributed volume offers a comprehensive discussion on how to design and discover pharmaceuticals using computational modeling techniques. The different chapters deal with the classical and most advanced techniques, theories, protocols, databases, and tools employed in computer-aided drug design (CADD) covering diverse therapeutic classes. Multiple components of Structure-Based Drug Discovery (SBDD) along with its workflow and associated challenges are presented while potential leads for Alzheimer’s disease (AD), antiviral agents, anti-human immunodeficiency virus (HIV) drugs, and leads for Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) disease are discussed in detail. Computational toxicological aspects in drug design and discovery, screening adverse effects, and existing or future in silico tools are highlighted, while a novel in silico tool, RASAR, which can be a major technique for small to big datasets when not much experimental data are present, is presented. The book also introduces the reader to the major drug databases covering drug molecules, chemicals, therapeutic targets, metabolomics, and peptides, which are great resources for drug discovery employing drug repurposing, high throughput, and virtual screening. This volume is a great tool for graduates, researchers, academics, and industrial scientists working in the fields of cheminformatics, bioinformatics, computational biology, and chemistry.

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

Matrix metalloproteinases (MMPs) have been established as promising biomolecular targets for novel drug design and discovery against numerous major disease conditions including various cancers, cardiovascular, neurodegenerative, inflammatory diseases, and more. This book covers various modern molecular modeling methodologies particularly related to MMP inhibitors. Included in the text are descriptions of ligand-based drug designing and structure-based drug designing modeling strategies for designing potential and target specific or selective MMPIs. This book will benefit those who are looking for an in-depth text on the design and discovery processes of novel and selective MMPIs. Features Describes modeling strategies applied to MMPs Elaborates on the designing strategies of MMPs specifically Includes in-depth analyses of related case studies Acts as a guide for medicinal chemists, not only from pharmaceutical industries, but also from academia Covers various modern molecular modeling methodologies, particularly related to MMPIs

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