Inamuddin - Drug Design Using Machine Learning

Scrivener Publishing

100 Cummings Center, Suite 541J

Beverly, MA 01915-6106

Publishers at Scrivener

Martin Scrivener ()

Phillip Carmical ()

Edited by

Inamuddin

Tariq Altalhi

Jorddy N. Cruz

and

Moamen Salah El-Deen Refat

This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

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Library of Congress Cataloging-in-Publication Data

ISBN 9781394166282

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Traditionally, the design of new drugs has been a long process that requires an investment of billions of dollars. In the last decades, molecular modeling techniques have been used in the pharmaceutical industry and large laboratories to accelerate this process in order to reduce the amount of money that needs to be invested. More recently, machine learning approaches to drug discovery have aroused great interest in the scientific community. This has occurred, among other reasons, due to advances in high-performance computing and the availability of an abundance of biological and chemical information on thousands of compounds. Through machine learning and artificial intelligence approaches, this information can be filtered quickly and at a low cost. From this, algorithms can be trained to be used in the different stages of drug design.

The objective of this book is to bring together several chapters that function as an overview of the use of machine learning and artificial intelligence applied to drug development. The initial chapters discuss drug-target interactions through machine learning for improving drug delivery, healthcare, and medical systems. Further chapters also provide topics on drug repurposing through machine learning, drug designing, and ultimately discuss drug combinations prescribed for patients with multiple or complex ailments. This book should be useful for information technology professionals, pharmaceutical industry workers, engineers, university students and faculty members, medical practitioners, researchers and laboratory workers who have a keen interest in the area of machine learning and artificial intelligence approaches applied to drug advancements. A chapter-by-chapter summary of the work reported in the 12 chapters of this book follows.

• describes the use of molecular recognition for drug development through various mathematical models. Molecular docking of the main elements is discussed in detail to consider which elements are important for obtaining a reliable prediction of protein-ligand complexes. The role of machine learning in molecular recognition models is also analyzed.
• reviews classical and machine learning-based approaches to study target-drug interactions in the field of drug discovery, from target identification to the optimization of the lead compound. In addition, a special section discusses peptide-based drugs.
• gives a brief overview of the various machine learning techniques that underpin artificial intelligence, poly-pharmacology, and drug repurposing to improve healthcare services. The way in which machine learning is used throughout the drug development process to help increase its efficacy and robustness, resulting in a significant reduction of the time and cost of bringing new drugs to market, is discussed in detail.
• elaborates on the advancements in artificial intelligence technologies along with their applications, enumerates the challenges faced by these technologies that retard their full-scale implementation, and also provides an overview of their social, legal, and economic aspects. All these are discussed for various applications such as drug delivery, healthcare, and medical systems.
• details the various machine learning approaches for drug repurposing. The network-based approach, text mining-based approach, and semantics-based approach are discussed in fair detail. Furthermore, case studies of drugs repurposed through machine learning programs are also discussed.
• summarizes the recent developments in the machine learning-mediated drug discovery process within industrial and academic contexts. More precisely, machine learning algorithms used for drug discovery, bioactivity prediction using machine learning, and application of machine learning in chemo-informatics are reviewed. Furthermore, an in-depth analysis of the challenges and suggestions are provided.
• details the basic principles underlying supercritical fluid technology and its main techniques. Furthermore, the more representative biodegradable polymers impregnated with supercritical fluid technology are described. Finally, the state of the art of supercritical fluid technology for improving drug absorption in biopolymer and the delivery processes are thoroughly reviewed.
• details the different in vivo, in vitro and in silico techniques applied to study the protein-protein interactions through the active sites. The role of the active sites of protein is also discussed. Moreover, databases and algorithms are mentioned along with their uses and advantages.