快速适应的图形神经网络,具有在临床前和临床数据中预测药物反应的先验知识
Hui Guo1, Xiang Lv1, Shenghao Li1
1College of Chemistry, Sichuan University, Chengdu, 610064, China.
Journal of pharmaceutical analysis
|November 17, 2025
概括
一种新的几次射击学习模型,metaDRP,在有限的数据上提高了药物反应预测的准确性. 它有效地解决了分布之外的挑战,帮助从实验室到诊所的翻译.
科学领域:
- 计算生物学是一种计算生物学.
- 药物基因组学 药物基因组学
- 机器学习在药物发现中的作用
背景情况:
- 药物反应预测对于有效的药物开发至关重要.
- 目前的模型面临的挑战是有限的实验数据和体外和体内环境之间的差异.
- 分布之外的问题阻碍了计算预测的转化到临床应用.
研究的目的:
- 引入一种新的几次射击学习模型,metaDRP,用于增强药物反应预测.
- 在各种药物-组织任务中使用有限的样本大小来提高预测准确性.
- 为了减轻分布外的问题,以便可靠地翻译研究结果.
主要方法:
- 开发了metaDRP,这是一个药物反应预测的meta-learner,利用了几次射击的学习原则.
- 对癌症药物敏感性基因组学 (GDSC) 和体内数据集的评估metaDRP.
- 评估模型处理有限数据和销售外情景的能力.
主要成果:
- 在GDSC和体内数据集上,metaDRP的性能与最先进的模型相美.
- 该模型有效地缓解了分布外问题,提高了预测可靠性.
- 在阐明特定药物的药物作用机制方面证明了metaDRP的可解释性.
结论:
- 在药物反应预测中,metaDRP提供了一种有前途的方法来克服数据稀缺和分布外的挑战.
- 该模型有助于可靠地将计算预测从受控环境转化为临床应用.
- 在计算药物发现和开发中促进少量学习的整合.
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