A Nature Biotechnology–reported effort showed deep‑learning models can accelerate identification of novel antibacterial scaffolds by mining chemical and biological data to prioritize candidates with activity against resistant pathogens. The study combined model‑driven screening with experimental validation to shorten the lead identification timeline and uncover chemotypes that evade common resistance mechanisms. Authors position the approach as complementary to traditional medicinal chemistry and screening, noting it can expand the accessible chemical space for antimicrobial discovery.