Harnessing AI for Drug Discovery: Machine Learning Perspectives

Developing new drugs is one of modern biomedicine's most complex and multi-step tasks. This process requires synthesizing multidisciplinary knowledge, ranging from molecular biology and biochemistry to clinical pharmacology and bioinformatics. Today, in the context of increasing disease complexity and the emergence of new therapeutic targets, traditional drug discovery methods are complemented by advanced technologies such as high-throughput screening (HTS), machine learning, and molecular modelling.The main goal of this book is to provide readers with a deep understanding of all stages of the drug discovery process. We will consider the ways of identifying potential therapeutic targets and compounds, including methods of structural biology and rational drug design. Particular attention is paid to the process of optimizing lead compounds, which includes drug chemistry, toxicology, and pharmacokinetics, as well as adsorption, distribution, metabolism, and excretion (ADME) issues.