Microorganisms produce a wealth of specialized metabolites, which play important roles in microbiome ecology and provide a rich resource for natural product drug discovery. Genome sequence data has revealed that only a tiny fraction of the chemical diversity of these natural products has been unearthed. Here, I will highlight recent work performed in my research group and with the wider community on developing and applying computational and artificial intelligence approaches to accelerate natural product discovery using omics data. Specifically, I will highlight new methods to chart biosynthetic diversity, to predict chemical (sub)structures of metabolites directly from sequence data, to retrobiosynthesise chemical substructures into their constituent building blocks, and to then connect biosynthetic gene clusters to molecules based on this information. Furthermore, I will highlight new methods to predict functions of natural products based on various types of omics data, to aid in the prioritization of the most high-potential areas within biosynthetic space. All in all, these computational approaches will facilitate smarter and more targeted automated genome mining of natural products to uncover the hidden chemistry of life and identify candidate mechanisms for microbiome-associated phenotypes of interest.

Marnix Medema is a Professor of Bioinformatics at Wageningen University, The Netherlands. He obtained a Biology BSc (Radboud University Nijmegen, 2006) and a Biomolecular Sciences MSc (University of Groningen, 2008). In 2013, he completed his PhD with Eriko Takano and Rainer Breitling in Groningen; during this period, he was also a visiting fellow with Michael Fischbach at the University of California, San Francisco. Following a postdoc at the Max Planck Institute for Marine Microbiology in Bremen, Germany, he joined Wageningen University in 2015. Since 2020, he is also affiliated with Leiden University as a professor of Bioinformatics. His group develops computational methodologies to unravel natural product biosynthesis using omics data, and applies these methods to the study of molecular interactions in microbiomes.