良好的机器学习实践:从现代制药发现企业的学习
Vladimir Makarov1, Christophe Chabbert2, Elina Koletou3
1The Pistoia Alliance, 401 Edgewater Place, Suite 600, Wakefield, MA, 01880, USA.
Computers in biology and medicine
|May 24, 2024
概括
这项研究确定了制药AI和机器学习 (ML) 采用的23个常见业务问题. 它提出了良好的机器学习实践,以克服这些挑战,以实现有效的药物发现和开发.
科学领域:
- 制药行业 制药行业 制药行业
- 药物的发现和开发.
- 人工智能 (AI) 是一种人工智能.
- 机器学习 (ML) 是指机器学习.
背景情况:
- 人工智能和机器学习在现代药物发现和开发中至关重要.
- 制药团队在有效实施AI/ML解决方案方面面临着挑战.
- 需要了解并解决这些实施障碍.
研究的目的:
- 系统地分析与制药行业AI和ML相关的业务实践.
- 确定人工智能/ML技术工作人员所面临的共同挑战.
- 为克服这些已识别的问题提出最佳实践建议.
主要方法:
- 进行了对人工智能和机器学习实践的全行业评估.
- 在制药发现中对个人进行了系统的业务分析.
- 研究了这些人与AI和ML技术的互动.
主要成果:
- 鉴定了使用AI/ML的制药专业人员遇到的23个常见的业务问题.
- 详细介绍了与交付AI/ML解决方案相关的具体挑战.
- 建立了良好的机器学习实践框架.
结论:
- 解决已识别的业务问题是成功将AI/ML整合到制药行业的关键.
- 良好的机器学习实践为克服实施挑战提供了一条途径.
- 优化AI/ML采用可以提高药物发现和开发的效率.
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