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The use of machine learning algorithms in drug discovery has accelerated in recent years and this book provides an in-depth overview of the still-evolving field.
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 excellent overview
Audience
The book will be useful for information technology professionals, pharmaceutical industry workers, engineers, university researchers, medical practitioners, and laboratory workers who have a keen interest in the area of machine learning and artificial intelligence approaches applied to drug advancements.
Inamuddin, PhD, is an assistant professor at King Abdulaziz University, Jeddah, Saudi Arabia and is also an assistant professor in the Department of Applied Chemistry, Aligarh Muslim University, Aligarh, India. He has extensive research experience in multidisciplinary fields of analytical chemistry, materials chemistry, electrochemistry, renewable energy and environmental science. He has published about 190 research articles in various international scientific journals, 18 book chapters, and 60 edited books with multiple well-known publishers.
Tariq Altalhi is Head of the Department of Chemistry and Vice Dean of Science College at Taif University, Saudi Arabia. He received his PhD from the University of Adelaide, Australia in 2014. His research interests include developing advanced chemistry-based solutions for solid and liquid municipal waste management, converting plastic bags to carbon nanotubes, and fly ash to efficient adsorbent material. He also researches natural extracts and their application in generation of value-added products such as nanomaterials.
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