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一个深度强化学习平台,用于抗生素的发现.

Hanqun Cao1,2,3,4,5, Marcelo D T Torres2,3,4,5, Jingjie Zhang1

  • 1Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

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|October 3, 2025
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概括

抗菌素耐药性需要新的抗生素. 一个深度学习框架,ApexAmphion,快速设计具有高疗效和广谱活性的新型抗生素,向细菌膜.

关键词:
在ApexAmphion中,我们使用的是ApexAmphion.深度学习是一种深度学习.抗生素 抗生素是一种抗生素.抗微生物类的抗微生物.人工智能的人工智能是人工智能.机器学习是机器学习.质设计的设计.蛋白质语言模型的模型强化学习是一种强化学习.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 药物发现 药物发现
  • 传染性疾病 传染性疾病

背景情况:

  • 抗菌素耐药性 (AMR) 构成了全球健康的重大威胁,预计到2050年,每年将导致1000万死亡.
  • 对新型抗生素的迫切需求推动了对创新药物发现平台的研究.

研究的目的:

  • 介绍ApexAmphion,这是一个深度学习框架,用于抗生素的新设计.
  • 为了证明该框架能够快速产生强效和广泛的抗微生物化合物.

主要方法:

  • 将一个大型蛋白质语言模型与强化学习结合起来,用于抗生素设计.
  • 对抗微生物数据的模型进行微调,并通过近接政策优化进行优化.
  • 使用复合奖励函数,整合最小抑制度 (MIC) 预测和物理化学性质.

主要成果:

  • 在实验室中对100种设计的评估表明,在低MIC值 (纳米范围) 的情况下,100%的命中率.
  • 99%的设计化合物表现出对临床相关细菌的广泛活性.
  • 分子有效地向细胞质膜杀死细菌.

结论:

  • ApexAmphion为新的抗生素发现提供了一个可扩展和快速的途径.
  • 该框架将生成,评分和多目标优化整合到一个管道中.
  • 这个平台促进了代设计,以提高新型抗生素的功效和可开发性.