Big Data in Drug Discovery

Abstract

Drug discovery is a challenging and complicated process that requires decades to discover and develop a drug. This process can be streamlined and simplified by using big data. Big data is applied in both chemical and biological aspects of drug discovery starting from target validation to the final stage of clinical trials and explained with an example focusing on medicinal chemistry. This chapter lists the public, commercial, and proprietary chemical space available for drug discovery. The application of AI in drug discovery provides greater support and improves its efficiency. The important softwares, scripts, data analysis, and mining tools available which aid in the identification of a novel drug and development are provided. Among the different stages in drug discovery, hit identification for a therapeutic target is an important and time-consuming phase. This can be handled by using proprietary and commercial screening platforms with the help of big data analysis and management. Thus, the importance of screening platforms and their success stories are highlighted with case studies. Overall, the integration of big data with AI-based tools greatly improves the efficiency of the drug discovery process by making it simple and less time-consuming. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.