Advances in high-throughput drug screening based on pharmacotranscriptomics

This review aims to summarize the developmental trajectory and research advancements in PTDS, with a focus on large-scale pharmacotranscriptomics profiling and artificial intelligence-driven data mining. It elucidates the appropriate application fields of PTDS in comparison to traditional drug screening paradigms, thereby providing novel perspectives for the technological evolution and implementation of PTDS. Key scientific concepts of review: PTDS can detect gene expression changes following drug perturbation in cells on a large scale and analyze the efficacy of drug-regulated gene sets, signaling pathways, and even complex diseases by combining artificial intelligence. The technical evolution of PTDS is systematically summarized, encompassing advancements in high-throughput PTDS detection technologies and data analysis methods. PTDS is categorized into microarray, targeted transcriptomics, and RNA-seq. Data analysis of PTDS involves ranking, unsupervised learning, and supervised learning algorithms. All these methods remain active in research and industry, coexisting to address evolving drug screening needs. On this basis, the roles of PTDS in promoting pathway-based drug screening strategies are deeply explored for drug discovery and drug combination design. Meanwhile, we also focus on the application of PTDS in screening and mechanism analysis of traditional Chinese medicine (TCM), which reflects that PTDS is suitable for detecting the complex efficacy of drugs, especially TCM. PTDS is an important development direction for high-throughput screening. By combining with artificial intelligence, PTDS will greatly revolutionize our understanding of drug screening and promote new drug research and development.