This title covers a wide range of topics relevant to the development of drugs. It provides a comprehensive description of the major methodological strategies available for rational drug discovery.

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.

Computational Approaches in Drug Discovery, Development and Systems Pharmacology provides detailed information on the use of computers in advancing pharmacology. Drug discovery and development is an expensive and time-consuming practice, and computer-assisted drug design (CADD) approaches are increasing in popularity in the pharmaceutical industry to accelerate the process. With the help of CADD, scientists can focus on the most capable compounds so that they can minimize the synthetic and biological testing pains. This book examines success stories of CADD in drug discovery, drug development and role of CADD in system pharmacology, additionally including a focus on the role of artificial intelligence (AI) and deep machine learning in pharmacology. Computational Approaches in Drug Discovery, Development and Systems Pharmacology will be useful to researchers and academics working in the area of CADD, pharmacology and Bioinformatics. - Explains computer use in pharmacology using real-life case studies - Provides information about biological activities using computer technology, thus allowing for the possible reduction of the number of animals used for research - Describes the role of AI in pharmacology and applications of CADD in various diseases

Nuclear receptors (NR) are ligand-induced activated transcription factors that are involved in numerous biological processes. Since the 1990's when the first structures were determined by means of X ray diffraction, the number of NR structures has increased considerably. Moreover several 'omics' projects (genomics, pharmcogenomics and proteomics) have opened up great opportunities for the discovery of new targets, the characterization of abnormal protein patterns, the selection of "tailored" drugs and the evaluation of drug efficacy even with a lack of structural data. Furthermore, structure-based drug design, computational methods for in silico screening and nanobiotechnology- based tools are simplifying this time-consuming and money-intensive research of lead compounds and, possibly, new drugs. Biological interactions such as those that occur between a protein and ligand are concerted events where flexible molecules interact. Thus understanding flexibility of large molecules or biological complexes is of primary importance to help define the right model to approximate the reality for drug discovery, virtual screening, food safety analysis, etc. NRs are known as flexible targets, with many structural similarities, in particular for their Ligand Binding Domain: these similarities could be assumed to share behavioural qualities that belong to this class of compounds. Thus to supply a possible, complete and exhaustive answer to questions about the behaviour of NRs, their interactions with new potential drugs, endocrine disruptors such as animal and human food toxins, food additives or industry residuals, it is mandatory to approach the problem from a different point of view: a molecular modelling approach, steered synthesis, and in vitro and in vivo tests, etc. The aim of this book is to provide a state of the art review on investigations into Nuclear Receptors.

This book is the first to combine computational material science and modeling of molecular solid states for pharmaceutical industry applications. • Provides descriptive and applied state-of-the-art computational approaches and workflows to guide pharmaceutical solid state chemistry experiments and to support/troubleshoot API solid state selection • Includes real industrial case examples related to application of modeling methods in problem solving • Useful as a supplementary reference/text for undergraduate, graduate and postgraduate students in computational chemistry, pharmaceutical and biotech sciences, and materials science

Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.