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

Drug repurposing is defined as identifying new pharmacological indications from old, existing, failed, investigational, already marketed, or FDA-approved drugs and prodrugs, and applying these new uses in the treatment of diseases other than the drug’s original intended therapeutic use. The application of computational techniques in discovery research not only helps in the development of drugs from leads or existing drug molecules but can also be useful for the repurposing of existing drug candidates. This new volume presents exciting recent advances in drug repurposing and computational approaches for the discovery and development of drugs against certain difficult-to-treat and life-threatening diseases. With contributions from a global team of experts (academicians, scientists, and researchers), it explores the sophisticated tools and techniques of drug repurposing and computational drug discovery. It delivers valuable information on computational techniques, tools, and databases being utilized for drug repurposing and for identifying the uses of existing drug candidates on different emerging or deadly diseases. Drug repurposing and computational approaches addressed in the book target the discovery and development of drugs for microbial infections (bacterial, fungal, viral, COVID-19), parasitic diseases and neglected tropical diseases (NTDs), malignant diseases (cancer), inflammatory diseases, cardiovascular disorders, diabetes, and aging and neurological (CNS) disorders. In addition, the challenges and regulatory issues encountered in drug repurposing and computational drug discovery programs are looked at, offering perspectives for future directions.

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

The world of pharmaceutical research is moving at lightning speed, and the age-old approach to drug discovery faces many challenges. It's a fascinating time to be on the cutting edge of medical innovation, but it's certainly not without its obstacles. The process of developing new drugs is often time-consuming, expensive, and fraught with uncertainty. Researchers are constantly seeking ways to streamline this process, reduce costs, and increase the success rate of bringing new drugs to market. One promising solution lies in the convergence of pharmacy science and engineering, particularly in computational drug discovery. Converging Pharmacy Science and Engineering in Computational Drug Discovery presents a comprehensive solution to these challenges by exploring the transformative synergy between pharmacy science and engineering. This book demonstrates how researchers can expedite the identification and development of novel therapeutic compounds by harnessing the power of computational approaches, such as sophisticated algorithms and modeling techniques. Through interdisciplinary collaboration, pharmacy scientists and engineers can revolutionize drug discovery, paving the way for more efficient and effective treatments. This book is an invaluable resource for pharmaceutical scientists, researchers, and engineers seeking to enhance their understanding of computational drug discovery. This book inspires future innovations by showcasing cutting-edge methodologies and innovative research at the intersection of pharmacy science and engineering. It contributes to the ongoing evolution of pharmaceutical research. It offers practical insights and solutions that will shape the future of drug discovery, making it essential reading for anyone involved in the pharmaceutical industry.

This book encapsulates recent applications of CI methods in the field of computational oncology, especially cancer diagnosis, prognosis, and its optimized therapeutics. The cancer has been known as a heterogeneous disease categorized in several different subtypes. According to WHO’s recent report, cancer is a leading cause of death worldwide, accounting for over 10 million deaths in the year 2020. Therefore, its early diagnosis, prognosis, and classification to a subtype have become necessary as it facilitates the subsequent clinical management and therapeutics plan. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning, and nature-inspired algorithms, have been widely utilized in various aspects of oncology research, viz. diagnosis, prognosis, therapeutics, and optimized clinical management. Appreciable progress has been made toward the understanding the hallmarks of cancer development, progression, and its effective therapeutics. However, notwithstanding the extrinsic and intrinsic factors which lead to drastic increment in incidence cases, the detection, diagnosis, prognosis, and therapeutics remain an apex challenge for the medical fraternity. With the advent in CI-based approaches, including nature-inspired techniques, and availability of clinical data from various high-throughput experiments, medical consultants, researchers, and oncologists have seen a hope to devise and employ CI in various aspects of oncology. The main aim of the book is to occupy state-of-the-art applications of CI methods which have been derived from core computer sciences to back medical oncology. This edited book covers artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary algorithms, various nature-inspired algorithms, and hybrid intelligent systems which are widely appreciated for the diagnosis, prognosis, and optimization of therapeutics of various cancers. Besides, this book also covers multi-omics exploration, gene expression analysis, gene signature identification of cancers, genomic characterization of tumors, anti-cancer drug design and discovery, drug response prediction by means of CI, and applications of IoT, IoMT, and blockchain technology in cancer research.

“Omics for Personalized Medicine” will give to its prospective readers the insight of both the current developments and the future potential of personalized medicine. The book brings into light how the pharmacogenomics and omics technologies are bringing a revolution in transforming the medicine and the health care sector for the better. Students of biomedical research and medicine along with medical professionals will benefit tremendously from the book by gaining from the diverse fields of knowledge of new age personalized medicine presented in the highly detailed chapters of the book. The book chapters are divided into two sections for convenient reading with the first section covering the general aspects of pharmaocogenomic technology that includes latest research and development in omics technologies. The first section also highlights the role of omics in modern clinical trials and even discusses the ethical consideration in pharmocogenomics. The second section is focusing on the development of personalized medicine in several areas of human health. The topics covered range from metabolic and neurological disorders to non-communicable as well as infectious diseases, and even explores the role of pharmacogenomics in cell therapy and transplantation technology. Thirty-four chapters of the book cover several aspects of pharmacogenomics and personalized medicine and have taken into consideration the varied interest of the readers from different fields of biomedical research and medicine. Advent of pharmacogenomics is the future of modern medicine, which has resulted from culmination of decades of research and now is showing the way forward. The book is an honest endeavour of researchers from all over the world to disseminate the latest knowledge and knowhow in personalized medicine to the community health researchers in particular and the educated public in general.

Volume 1 of Computational Approaches in Bioengineering—Computational Approaches in Biotechnology and Bioinformatics—explores many significant topics of biomedical engineering and bioinformatics in an easily understandable format. It explores recent developments and applications in bioinformatics, biomechanics, artificial intelligence (AI), signal processing, wearable sensors, biomaterials, cell biology, synthetic biology, biostatistics, prosthetics, big data, and algorithms. From applications of biomaterials in advanced drug delivery systems to the role of big data, AI, and machine learning in disease diagnosis and treatment, the book will help readers understand how these technologies are being applied across the areas of biomedical engineering, bioinformatics, and healthcare. The chapters also include case studies on the role of medical robots in surgery and the determination of protein structure using genetic algorithms. The contributors are all leading experts across multiple disciplines and provide chapters that truly represent a complete view of these state-of-the-art technologies. FEATURES Covers a wide range of subjects from biomedical engineering like wearable devices, biomaterials, synthetic biology, phytochemical extraction, and prosthetics Explores AI, machine learning, big data analysis, and algorithms in biomedical engineering and bioinformatics in an easily understandable format Includes case studies on the role of medical robots in surgery and the determination of protein structure using genetic algorithms Discusses genetic diagnosis, classification, and risk prediction in cancer using next-generation sequencing in oncology This book is ideally designed for biomedical professionals, biomedical engineers, healthcare professionals, data engineers, clinicians, physicians, medical students, hospital directors, clinical researchers, and others who work in the field of artificial intelligence, bioinformatics, and computational biology.

Computational Approaches for Novel Therapeutic and Diagnostic Designing to Mitigate SARS-CoV2 Infection: Revolutionary Strategies to Combat Pandemics compiles information about various computational bioinformatic approaches that can help combat viral infection. The book includes working knowledge of various molecular docking and molecular dynamic simulation approaches that have been exploited for drug repurposing and drug designing purpose. In addition, it sheds light on reverse vaccinomics and immunoinformatic approaches for vaccine designing against SARS-CoV2 infection. This book is an essential resource for researchers, bioinformaticians, computational biologists, computational chemists and pharmaceutical companies who are working on the development of effective and specific therapeutic interventions and point-of-care diagnostic devices using various computational approaches. - Covers computational based approaches for designing and repurposing drugs - Discusses immunoinformatic and reverse vaccinomic approaches for effective vaccine design - Categorizes information about artificial intelligence-based drug screening and diagnostic tools

This work presents the latest development in the field of computational intelligence to advance Big Data and Cloud Computing concerning applications in medical diagnosis. As forum for academia and professionals it covers state-of-the-art research challenges and issues in the digital information & knowledge management and the concerns along with the solutions adopted in these fields.