评估机器学习和人工智能在瘤学中的作用 药物重定向努力
1Imperial College Business School, Imperial College London, United Kingdom.
Journal of research in pharmacy practice
|December 10, 2025
概括
机器学习 (ML) 可以推进瘤药物重定向,但克服数据质量,监管障碍和商业模式挑战对于成功至关重要. 这项研究探讨了关于在药物发现中整合ML的专家观点.
科学领域:
- 在瘤学瘤学.
- 药物发现 药物发现 药物发现
- 人工智能的人工智能
背景情况:
- 制药研发部门面临着效率低下和经济压力.
- 药物重定向提供了一个潜在的解决方案,以简化发现.
- 机器学习 (ML) 越来越多地被探讨其在药物重新利用中的作用.
研究的目的:
- 调查ML对瘤药物重新使用的贡献.
- 探索 ML 在这个领域的现实应用,挑战和未来潜力.
- 了解利益关方关于将ML纳入瘤药物重定向的观点.
主要方法:
- 采用"研究洋"框架,采用解释主义哲学和归纳方法.
- 使用多种方法策略:叙事文献评论和13个半结构面试.
- 通过 NVivo 的支持,使用 Braun 和 Clarke 的框架进行了采访数据的专题分析.
主要成果:
- 确定了技术 (数据质量,可访问性,基础设施),监管 (数据治理,市场保护) 和商业 (利能力,竞争,数据所有权) 领域的关键挑战.
- 技术障碍包括数据质量差以及对现实世界数据集的访问有限.
- 监管和商业挑战涉及道德数据治理,市场排他性,利能力和分散的数据所有权.
结论:
- 机器学习在推进瘤药物重定向方面具有重大潜力.
- 实现ML的好处需要解决关键的技术,监管和经济挑战.
- 需要合作和经济可持续的模型来促进ML在药物发现中的整合.
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