Toward an integrated omics approach for plant biosynthetic pathway discovery in the age of AI.

Abstract

Elucidating plant biosynthetic pathways is key to advancing a sustainable bioeconomy by enabling access to complex natural products through synthetic biology. Despite progress from genomic, transcriptomic, and metabolomic approaches, much multiomics data remain underutilized. This review highlights state-of-the-art multiomics strategies for discovering plant biosynthetic pathways, addressing challenges in data acquisition and interpretation with emerging computational tools. We propose an integrated workflow combining molecular networking, reaction pair analysis, and gene expression patterns to enhance data utilization. Additionally, artificial intelligence (AI)-driven approaches promise to revolutionize pathway discovery by streamlining data analysis and validation. Integrating multiomics data, chemical insights, and advanced algorithms can accelerate understanding of plant metabolism and bioengineering valuable natural products efficiently.