A promising antimicrobial hit is only the start. The harder questions are whether activity holds across strains and conditions, what biology underlies the response and how readily microorganisms may adapt. Used with a clear purpose, genomics, transcriptomics, metabolomics and metagenomics can connect activity with genotype, regulation, metabolism and community context. Their value lies not in generating data for its own sake, but in reducing uncertainty around a candidate and guiding the next experiment, validation step or development decision.
Antimicrobial Discovery Needs More Than New Hits
The search for new antimicrobials is becoming broader and more technically ambitious, with genome and metagenome mining, AI-assisted design and non-traditional approaches now sitting alongside conventional screening. One recent study predicted nearly one million antimicrobial peptides from microbial sequences across the global microbiome, a measure of how fast the searchable biological space is expanding.1
Metagenomics is helping drive that expansion by opening access to uncultivated microbial diversity and revealing biosynthetic gene clusters, antimicrobial peptides and resistance mechanisms beyond conventional culture-based approaches.
Yet, a larger search space does not necessarily produce a stronger development pipeline. The World Health Organization identified 90 antibacterial candidates in clinical development in 2025, but only 15 met its criteria for innovation.2 This gap between the growing ability to find activity and the continuing scarcity of differentiated candidates remains a critical challenge.
An initial hit shows that something happened under a defined set of conditions. It does not yet establish whether the effect is reproducible, biologically meaningful or robust enough to justify further investment. That is where discovery shifts from finding activity to understanding it.
