通过预先训练和微调的基于学习的几次射击管道发现针对Acinetobacter baumannii的抗微生物
Junjie Huang1,2, Wentao Zhang3, Aowen Wang3
1MOE Key Laboratory of Macromolecule Synthesis and Functionalization of Ministry of Education, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, PR China.
研究人员开发了一种几次射击的学习管道,以发现新型抗微生物 (AMP) 对抗多药耐药的Acinetobacter baumannii.有效. 该方法成功识别了具有低毒性和耐药性易感性的强效AMP,提供了一个新的治疗范式.
科学领域:
- 微生物学 微生物学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 宝曼尼菌 (Acinetobacter baumannii) 是一种多抗药性病原体,导致难以治疗的医院感染.
- 对于对抗Acinetobacter baumannii.有效的新型抗微生物 (AMP) 有着至关重要的需求.
- 有限的培训数据在发现新的AMP方面构成了重大挑战.
研究的目的:
- 开发和验证一些射击学习管道,以识别针对Acinetobacter baumannii.强大的AMP.
- 为了克服抗微生物发现的数据稀缺性挑战.
- 发现具有低毒性和最小耐药性潜力的新型AMP.
主要方法:
- 采用了集成分类,排名和回归模块的一些射击学习管道.
- 模块使用有限的相似和真实数据进行了预训练和微调.
- 该管道扫描了六,七和八的大型图书馆.
主要成果:
- 该管道成功确定了针对Acinetobacter baumannii和Candida albicans的活性AMP.
- 发现的AMP显示出低的非标毒性和可以忽略不计的耐药性易感性.
- 一种新型,EME7(7),有效地治疗了小鼠中的Acinetobacter baumannii肺炎,而不会造成脏损伤.
结论:
- 短暂的学习管道在发现强大的AMP方面是有效的,尽管培训数据有限.
- 这种方法为开发针对多药耐药细菌的新疗法提供了有希望的策略.
- 这些发现为解决抗微生物药物发现中的数据稀缺问题提供了一个范式.
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