药物重用中的纯粹和混合协作过的全面评估
Clémence Réda1, Jill-Jênn Vie2, Olaf Wolkenhauer3,4,5
1Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051, Germany. clemence.reda@uni-rostock.de.
Scientific reports
|January 21, 2025
概括
药物重定向加速了使用现有化合物的药物发现. 我们提出了一种新的,可重复的方法,以公平评估和比较药物重用模型,确保可靠的预测新药与疾病的关联.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 药物开发是昂贵的,耗时的,并且失败率很高.
- 药物再利用提供了一个有效的替代方案,利用已批准的化合物.
- 预测新药与疾病的关联是药物重定向成功的关键.
研究的目的:
- 建立一个可复制的方法来比较协作过 (CF) 模型在药物重用.
- 为了应对由于缺乏共识方法的模型性能评估的挑战.
- 为药物重用模型提供公正和全面的绩效评估指导方针.
主要方法:
- 提出了一种可重复的基准方法来评估基于CF的药物重用.
- 在八个不同的药物重定向数据集中比较了11个不同的CF模型.
- 专注于评估模型推断能力和减轻数据泄露.
主要成果:
- 确定了当前评估实践中的潜在偏见和数据泄露问题.
- 证明了评估超越培训数据的模型推断的重要性.
- 发现了一类CF模型的子类别,这些CF模型是高效的,并且对分配转移具有强大的耐用性.
结论:
- 一个标准化,可重复的评估框架对于推动药物重用研究至关重要.
- 公平评估必须考虑数据泄露,并评估外推能力.
- 确定了可靠的CF方法,以进行强大的药物重定向预测.
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