Multi-Omics and Integrative Analytics in Natural Products Discovery

Summary

This review emphasizes cutting-edge multi-omics methodologies, including metabolomics, genomics, transcriptomics, and proteomics, which are integrated with artificial intelligence and machine learning, as well as network analysis, to enhance the discovery and functional validation of new natural products.

Abstract

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Introduction

Natural products, which are molecules produced by various organisms, including microbes and plants, have long served as a vital source of medicines, ranging from antibiotics like penicillin to anticancer drugs such as Taxol. A significant portion of our current antibiotics and chemotherapy agents is derived from these natural compounds¹. However, the traditional methods used to discover new NPs, such as culturing microbes and employing bioassay-guided isolation, have become increasingly difficult. In the late 20th century, pharmaceutical interest in NPs declined as companies faced diminishing returns; many compounds that were easy to find had a....