Antibiotic resistance is outrunning the discovery pipeline — so researchers taught machines to look where humans can’t. Stokes et al. ’trained a deep neural network capable of predicting molecules with antibacterial activity’ and surfaced halicin, ‘structurally divergent from conventional antibiotics.’ Liu et al. screened ~7,500 molecules and found abaucin, with ’narrow-spectrum activity against A. baumannii,’ a priority Gram-negative pathogen. Wong et al. made the black box talk — graph neural networks predicting activity and cytotoxicity for ‘12,076,365 compounds’ and yielding a new structural class ‘selective against methicillin-resistant S. aureus.’ Das et al. designed antimicrobials outright with ‘deep generative models and molecular dynamics simulations.’ And two teams went mining nature’s own arsenal: 181 of 216 gut-microbiome peptides proved active, and AMPSphere catalogued ‘863,498 non-redundant peptides’ from the global microbiome — an open resource. AI widens where we look for medicine.
