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机器学习可以恢复受损的制药3D打印配方数据.

Olima Uddin1, Yusuf Ali Mohammed1, Simon Gaisford2

  • 1School of Biological and Behavioural Sciences, Queen Mary University of London, Mile End Road, London E1 4DQ, UK.

International journal of pharmaceutics
|November 18, 2025
PubMed
概括
此摘要是机器生成的。

机器学习,特别是拒绝自动编码器 (DAE),可以有效地重建受损的制药配方数据,保护个性化药物生产免受网络攻击并确保患者安全.

关键词:
人工智能的人工智能数字性数字性药物开发 药物开发化沉积模型的建模质量控制 质量控制 质量控制

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科学领域:

  • 制药制造业 制药制造业 制药制造业
  • 数字健康数字健康
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 药品的3D打印使个性化医疗成为可能,但也带来网络安全风险.
  • 网络攻击可以破坏关键的药物配方数据,危及患者的安全.
  • 开发强大的数据保护方法对于数字制药制造至关重要.

研究的目的:

  • 调查无效自编码器 (DAE) 在重建损坏的药物配方数据中的有效性.
  • 模拟和解决影响数字药物制造数据的潜在网络攻击场景.
  • 提高药品数据完整性的数字弹性.

主要方法:

  • 使用了1,623种制药配方 (336种成分,>545,000个数据点) 的数据集.
  • 通过引入数据删除 (1%-50%) 和噪音注入来模拟网络攻击.
  • 评估了多个DAE配置的数据恢复性能.

主要成果:

  • DAE获得了高的R2分数 (0.989在1%的腐败,0.924在50%的腐败).
  • DAEs准确地重建了活性药物成分和辅助剂的值,表明有意义的模式识别.
  • 传统的机器学习方法无法恢复损坏的数据,与DAE不同.

结论:

  • 拒绝自动编码器有效地保护制药配方数据免受腐败.
  • DAE为提高制药制造业的数字弹性提供了一个有希望的解决方案.
  • 机器学习在数字制药行业的数据质量和患者安全方面发挥着至关重要的作用.