Huihong Wang: Pharmaceutical Department, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, P. R. China.
Jiale Huang: Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing, China. ORCID
Xianfu Fang: Pharmaceutical Department, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, P. R. China.
Mengyao Liu: Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing, China.
Xiaohong Fan: Pharmaceutical Department, Chongqing University Three Gorges Hospital, Chongqing University, Chongqing, P. R. China.
Yizhou Li: Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing, China.
INTRODUCTION: Drug discovery is a complex and multifaceted process driven by scientific innovation and advanced technologies. Next-Generation Sequencing (NGS) platforms, encompassing both short-read and long-read technologies, have revolutionized the field by enabling the high-throughput and cost-effective analysis of DNA and RNA molecules. Continuous advancements in NGS-based technologies have enabled their seamless integration across preclinical and clinical workflows in drug discovery, encompassing early-stage drug target identification, candidate selection, genetically stratified clinical trials, and pharmacogenetic studies. AREA COVERED: This review provides an overview of the current and potential applications of NGS-based technologies in drug discovery and development process, including their roles in novel drug target identification, high-throughput screening, clinical trials, and clinical medication studies. The review is based on literature retrieval from the PubMed and Web of Science databases between 2018 and 2024. EXPERT OPINION: As technologies advance rapidly, NGS enhances accuracy and generates vast datasets. These datasets are extensively integrated with other heterogeneous data in systems biology and are mined using machine learning to extract significant insights, thereby driving progress in drug discovery.
