Data-Driven Derivation of Molecular Substructures That Enhance Drug Activity in Gram-Negative Bacteria

The complex cell envelope of Gram-negative bacteria creates a formidable barrier to antibiotic influx. Reduced drug uptake impedes drug development and contributes to a wide range of drug-resistant bacterial infections, including those caused by extremely resistant species prioritized by the World Health Organization. To develop new and efficient treatments, a better understanding of the molecular features governing Gram-negative permeability is essential. Here, we present a data-driven approach, using matched molecular pair analysis and machine learning on minimal inhibitory concentration data from Gram-positive and Gram-negative bacteria to uncover chemical features that influence Gram-negative bioactivity. We find recurring chemical moieties, of a wider range than previously known, that consistently improve activity and suggest that this insight can be used to optimize compounds for increased Gram-negative uptake. Our findings may help to expand the chemical space of broad-spectrum antibiotics and aid the search for new antibiotic compound classes.革兰氏阴性菌（Gram-negative bacteria）复杂的细胞包膜，构成了阻碍抗生素进入菌体的强效屏障。细菌对药物的摄取能力下降，不仅会阻碍新药研发，还会促成多种耐药细菌性感染，其中包括被世界卫生组织（World Health Organization, WHO）列为优先应对的极端耐药菌种引发的感染。为开发新型高效治疗手段，深入理解调控革兰氏阴性菌通透性的分子特征至关重要。本研究提出一种数据驱动的研究方法：基于革兰氏阳性菌（Gram-positive bacteria）与革兰氏阴性菌的最低抑菌浓度（minimal inhibitory concentration, MIC）数据，结合匹配分子对分析与机器学习技术，挖掘影响革兰氏阴性菌生物活性的化学特征。我们发现了一类覆盖范围远超既往认知的化学基团，这类基团可持续提升化合物活性，并提示可借助该发现优化分子以增强其对革兰氏阴性菌的摄取效率。本研究成果或有助于拓展广谱抗生素的化学空间，并助力新型抗生素化合物类别的发掘工作。