There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.

需要发现和开发能够有效对抗代谢休眠细菌的无毒抗生素，这些细菌是慢性感染的基础并促进抗生素耐药性。传统抗生素的发现历来偏向于对代谢活跃的细胞有效的化合物，这种特性并不能预测在代谢不活跃的情况下的疗效。在这里，我们将固定相筛选方法与深度学习驱动的虚拟屏幕和毒性过滤相结合，以发现对代谢休眠细菌具有杀伤力和有利毒性特征的化合物。最有效且结构独特且没有任何明显机制责任的化合物是塞马莫德（semapimod），它是一种有效抵抗静止期E 的抗炎药。大肠杆菌和A .鲍曼氏菌。结合微生物测定、生化测量和单细胞显微镜，我们发现塞马莫德通过结合脂多糖选择性地破坏和透化细菌外膜。这项工作说明了利用非传统筛选方法和深度学习模型来识别在感染相关环境中有效的无毒抗菌化合物的价值。