Researchers at Harvard’s Wyss Institute used machine learning combined with classical screening to identify small molecules active against Neisseria gonorrhoeae in vagina-on-a-chip and mouse infection models. The findings were published June 17, 2026 in Science Translational Medicine. The approach aims to accelerate identification of candidates for drug-resistant gonorrhea by leveraging hybrid discovery workflows that connect in vitro and in vivo infection contexts. With gonorrhea continuing to drive antibiotic resistance concerns, the study highlights a pipeline-relevant platform for selecting molecules that demonstrate functional activity beyond traditional cell assays.