Artificial intelligence is a disruptive area which transforms cutting-edge healthcare technology to analyze clinical workflows, sharpen diagnostics, and improve precision medicine. The goal of this review is to identify approaches involving a collaborative examination to determine the key features influencing the adoption of artificial intelligence methodologies in advanced cutting-edge solutions for metabolomics and drug design. In clinical and translational settings, a comprehensive investigation of legal and ethical principles will be included to highlight the significance of omics analysis and drug design in the application of artificial intelligence tools with artificial intelligence in healthcare, the real-world uses, and difficulties tied to societal and regulatory issues. The real-world effects of artificial intelligence for researchers and technicians can provide guidance for tailored strategies focused on leveraging potential to improve high-dimensional data analysis on metabolomics and drug design. As artificial intelligence methodologies continue to evolve, efforts must be directed toward structured frameworks that uphold human oversight and engagement to optimize the utility of artificial intelligence algorithms and big data methodologies. The key contributions of this study include a comprehensive overview of cutting-edge artificial intelligence methodologies and software programs in metabolomics and drug design, and critical perspectives which can solidify the future directions in the development of algorithmic approaches to bridge metabolomics and drug design.
Kim et al. (Sun,) studied this question.