Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

鲍曼不动杆菌是一种医院内革兰氏阴性病原体，通常表现出多重耐药性。事实证明，通过传统的筛选方法发现针对鲍曼不动杆菌的新抗生素具有挑战性。幸运的是，机器学习方法可以快速探索化学空间，增加发现新抗菌分子的可能性。在这里，我们筛选了约 7,500 个在体外抑制鲍曼不动杆菌生长的分子。我们用这个生长抑制数据集训练了一个神经网络，并对具有抗鲍曼不动杆菌活性的新结构分子进行了计算机预测。通过这种方法，我们发现了鲍曼素，一种对鲍曼不动杆菌具有窄谱活性的抗菌化合物。进一步的研究表明，松柏素通过涉及 LolE 的机制扰乱脂蛋白运输。此外，鲍曼素可以控制小鼠伤口模型中的鲍曼不动杆菌感染。这项工作强调了机器学习在抗生素发现中的实用性，并描述了一种有前途的先导化合物，具有针对具有挑战性的革兰氏阴性病原体的靶向活性。