生成型人工智能赋予数字双胞胎在药物发现和临床试验中的权力
Maria Bordukova1,2,3, Nikita Makarov1,2,3, Raul Rodriguez-Esteban4
1Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany.
Expert opinion on drug discovery
|October 27, 2023
概括
数字双胞胎 (DTs) 利用生成AI进行虚拟药物开发和临床试验,提高效率和精确医学. 虽然目前的应用有限,但人工智能对制药研究的未来具有变革性的潜力.
科学领域:
- 生物技术是生物技术.
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 数字双胞胎 (DTs) 是用于药物开发和临床试验中的in silico模拟的虚拟表示.
- 数据技术简化了具有高经济,道德或社会负担的流程,提高了效率.
- 生成型人工智能 (AI) 能够创建复杂的,现实的数据来建模DTs.
研究的目的:
- 引入生成性人工智能及其在建模数字双胞胎 (DTs) 中的作用.
- 为了比较当前在药物发现和临床试验中对DT的生成AI实现.
- 讨论阻碍DT在制药研究中的全部潜力的挑战.
主要方法:
- 介绍DT建模中的生成AI原理和应用.
- 对药物发现和临床试验中的DT现有的生成AI工具进行比较分析.
- 讨论DT实施的技术和监管障碍.
主要成果:
- 生成型人工智能有助于创建复杂的DT模型.
- 目前在药物开发中的DT应用范围有限.
- 生成性人工智能具有显著的潜力,可以彻底改变药物发现和临床试验.
结论:
- 生成性人工智能是实现数字双胞胎在药物发现和临床试验中的全部潜力的关键.
- 克服技术和监管挑战对于广泛采用DT至关重要.
- 由人工智能驱动的DT有望推进精准医学并加速治疗开发.
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