转移学习使得从超大化学空间发现ESKAPE病原体的亚微分子抗菌剂成为可能
Miguel García-Ortegón1,2,3, Srijit Seal3,4, Emily Geddes4
1Statistical Laboratory, University of Cambridge Wilberforce Rd Cambridge CB3 0WA UK sb2116@cam.ac.uk miguel.garcia.ortegon@gmail.com.
Chemical science
|October 17, 2025
概括
研究人员开发了一个深度图形神经网络 (DGNN) 框架,以加快新抗生素的发现. 这种方法有效地从庞大的化学图书馆中识别出强有力的抗菌化合物,解决了对新型抗菌药物的关键需求.
科学领域:
- 药物的发现和开发.
- 计算化学是一种计算化学.
- 微生物学 微生物学
背景情况:
- 抗菌素耐药性 (AMR) 是一个主要的全球健康威胁,特别是来自格拉姆阴性ESKAPE病原体.
- 抗生素的发现受到有限的数据,复杂的细菌耐药机制和不良的经济激励因素的阻碍.
研究的目的:
- 引入使用深度图形神经网络 (DGNN) 来识别新型抗菌剂的转移学习框架.
- 为了克服数据短缺,加快超大型化学图书馆的选.
主要方法:
- 预先培训的DGNN在各种分子数据集上学习一般化学特征.
- 对有限的抗菌查数据进行了微调的DGNN.
- 从ChemDiv和Enamine库中几乎选了超过10亿种化合物.
主要成果:
- 转移学习方法显著改善了预测性表现和丰富因素.
- 确定了156种候选化合物,其中54%显示对大肠杆菌有抗菌活性.
- 几种已识别的化合物显示出广泛的疗效,亚微分子功效和低细胞毒性.
结论:
- 基于DGNN的转移学习框架对于发现强效,低毒性的抗菌化合物是有效的.
- 这种方法成功地在未经探索的化学空间中航行,并加速了抗生素的发现.
- 发布开源模型和工作流程,以促进未来对抗抗菌素耐药性的研究.
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