Methods and applications of machine learning in structure-based drug discovery

ABSTRACT

Structure-based drug discovery (SBDD) utilizes the three-dimensional (3D) structure of a target protein to identify the lead compounds. This medium is then considered a viable solution based on its availability and correlation with a particular disease. In the case of pandemics like COVID 19, shortening drug development time can save millions of people worldwide;for such a task, classical drug discovery methods will take a long time. Hence, researchers worldwide actively incorporated machine learning (ML) into the drug discovery process, particularly in SBDD, to minimize the lead optimization time. ML uses statistical methods to make a computer perform tasks, take a critical decision, and automate this entire process without being explicitly programmed. With this, the computer can discover new insights about data and unknown patterns crucial to decide the therapeutic use of lead compounds as drugs. The use of ML in the drug discovery field is not new, and it spans an ample research space. By integrating artificial intelligence with ML techniques, viable targets can be found using data clustering, regression, and classification from vast omics databases and sources. In this chapter, we will discuss the methods and applications of ML in SBDD. © 2022 Elsevier Inc. All rights reserved.