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Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug-target, target-disease, and ultimately drug-disease associations.

This book describes the theories, applications, and challenges for different oral controlled release formulations.This book differs from most in its focus on oral controlled release formulation design and process development.

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.

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. Graphic abstractThe primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Presenting both a panoramic introduction to the essential disciplines of drug discovery for novice medicinal chemists as well as a useful reference for veteran drug hunters, this book summarizes the state-of-the-art of medicinal chemistry. It covers key drug targets including enzymes, receptors, and ion channels, and hit and lead discovery. The book hen surveys a drug's pharmacokinetics and toxicity, with a solid chapter covering fundamental bioisosteres as a guide to structure-activity relationship investigations.

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Explains how to perform and analyze the results of the latest physicochemical methods With this book as their guide, readers have access to all the current information needed to thoroughly investigate and accurately determine a compound's pharmaceutical properties and their effects on drug absorption. The book emphasizes oral absorption, explaining all the physicochemical methods used today to analyze drug candidates. Moreover, the author provides expert guidance to help readers analyze the results of their studies in order to select the most promising drug candidates. This Second Edition has been thoroughly updated and revised, incorporating all the latest research findings, methods, and resources, including: Descriptions and applications of new PAMPA models, drawing on more than thirty papers published by the author's research group Two new chapters examining permeability and Caco-2/MDCK and permeability and the blood-brain barrier Expanded information and methods to support pKa determination New examples explaining the treatment of practically insoluble test compounds Additional case studies demonstrating the use of the latest physicochemical techniques New, revised, and expanded database tables throughout the book Well over 200 drawings help readers better understand difficult concepts and provide a visual guide to complex procedures. In addition, over 800 references serve as a gateway to the primary literature in the field, facilitating further research into all the topics covered in the book. This Second Edition is recommended as a reference for researchers in pharmaceutical RD as well as in agrochemical, environmental, and other related areas of research. It is also recommended as a supplemental text for graduate courses in pharmaceutics.

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.