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 - Provides a broad synopsis of machine learning and artificial intelligence applications to the advancement of drugs; - Details the use of molecular recognition for drug development through various mathematical models; - Highlights classical as well as machine learning-based approaches to study target-drug interactions in the field of drug discovery; - Explores computer-aided technics for prediction of drug effectiveness and toxicity.
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.
Table of Contents
PrefaceChapter 1 Molecular Recognition and Machine Learning to Predict Protein-Ligand Interactions
Chapter 2 Machine Learning Approaches to Improve Prediction of Target-Drug Interactions
Chapter 3 Machine Learning Applications in Rational Drug Discovery
Chapter 4 Deep Learning for the Selection of Multiple Analogs
Chapter 5 Drug Repurposing Based on Machine Learning
Chapter 6 Recent Advances in Drug Design with Machine Learning
Chapter 7 Loading of Drugs in Biodegradable Polymers Using Supercritical Fluid Technology
Chapter 8 Neural Network for Screening Active Sites on Proteins
Chapter 9 Protein Redesign and Engineering Using Machine Learning
Chapter 10 Role of Transcriptomics and Artificial Intelligence Approaches for the Selection of Bioactive Compounds
Chapter 11 Prediction of Drug Toxicity Through Machine Learning
Chapter 12 Artificial Intelligence for Assessing Side Effects
Index
