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在基于堆叠的机器学习模型的指导下,发现了针对Acinetobacter baumannii的新型多素E辅助剂

Yuce Chen¹, Kunkun Shen¹, Ting Lin²

¹Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology Shanghai 200237 China xyxu@ecust.edu.cn.

RSC medicinal chemistry

|January 12, 2026

概括

研究人员开发了一种机器学习模型,ADStack,以识别新型抗生素辅助剂. ADStack成功地确定了六种增强多素E的化合物.

科学领域:

计算化学和药理学计算化学和药理学
机器学习在药物发现中的作用
抗微生物耐药性研究的研究.

背景情况:

抗生素耐药性是一个关键的全球健康威胁,需要新的治疗策略.
作为最后手段的抗生素,聚米辛E具有剂量依赖的毒性,限制了其临床使用.
抗生素辅助剂可以提高疗效,减少抗生素剂量,减轻毒性.

研究的目的:

开发和验证用于识别新型抗生素增强剂的机器学习模型.
为了选一个大型的化合物库,寻找潜在的抗生素辅助剂.
为了评估已识别的辅助剂与多素E结合的疗效.

主要方法:

使用抗生素强化试验和PubChem-MACCS指纹选了1245种专有化合物.
开发了一个堆叠机器学习模型 (ADStack),结合随机森林和极端梯度增强.
应用ADStack来选9938种化合物的药物重用库,并验证了顶级候选者.

主要成果:

与基础分类器相比,ADStack在识别活性分子方面表现优越.
确定了六种新型化合物,这些化合物增强了对抗Acinetobacter baumannii的多素E活性.
化合物F26在体外和体外模型中与多素E结合时显示出显著的治疗疗效.
在分析证实了F26.6的良好的药理动力学和药物相似性.

结论:

机器学习,特别是ADStack,是发现新型抗生素辅助剂的强大工具.
化合物F26代表了一种有前途的辅助剂,可以增强对抗耐药细菌的多素E疗效.
这种方法提供了一种新的策略来应对抗生素耐药性的不断升级的危机.

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