[EN]Artificial Generative Intelligence (AGI) has exploded into biomedical and pharmaceutical research, fundamentally transforming the way scientists approach literature review, experiment design, and reagent and antibody selection. This article explores how IAG, supported by advanced machine learning and natural language processing models, has revolutionized these processes. The IAG streamlines literature review, extracting relevant information, identifying emerging patterns and trends in the scientific literature, and generating innovative hypotheses. It also acts as an advanced search tool, allowing researchers to quickly access accurate information in an ocean of data. A prominent example of this application is BenchSci, a platform that uses the IAG to recommend reagents and antibodies based on real experimental data and scientific literature. This integration of IAG into experimental design promises to accelerate research, reduce costs, and improve the precision of experiments. Together, the IAG is presented as a catalyst for discoveries in pharmaceutical and biomedical research, offering unprecedented potential to advance the understanding and treatment of diseases, and improve decision-making in the industry.
