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用于药物再利用的知识图的使用:从经典的机器学习算法到图形神经网络

Siqi Wei¹, Christo Sasi², Jelle Piepenbrock³

¹Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.

Computers in biology and medicine

|August 10, 2025

概括

药物重用确定了现有药物的新用途. 知识图 (KGs) 与人工智能和机器学习相结合,提供了强大的计算方法来预测药物-疾病关系并加速药物发现.

科学领域:

生物医学信息学是生物医学信息学.
计算药理学是一种计算药理学.
人工智能在药物发现中的作用

背景情况:

药物重定向通过确定现有药物的新疗法来加速新疗法的开发.
包括人工智能 (AI) 在内的计算方法对于发现药物重用候选人越来越重要.
知识图 (KG) 提供了一个强大的框架,用于建模复杂的生物医学知识和预测药物和疾病的关联.

研究的目的:

提供对利用知识图表的计算药物重定向方法的全面审查.
探索在药物发现中使用基于KG的表示背后的逻辑.
分析各种基于KG的工具,它们的方法和预测药物疾病联系的性能.

主要方法:

对基于KG的计算药物重新利用现有文献的审查和综合.
讨论传统的机器学习和深度学习技术,应用于KG用于药物重用.
分析特定的基于KG的工具,重点关注它们的构建,链接预测准确性和限制.

主要成果:

基于KG的方法提供了一个直观的方法来整合和利用各种生物医学数据用于药物重新用途.
传统的机器学习和先进的深度学习模型都在基于KG的链接预测中显示出有前景,用于识别药物疾病关系.
基于KG的各种工具存在,每个工具在数据集成和预测能力方面都有独特的优缺点.

结论:

知识图是通过有效地建模生物医学知识来推动计算药物重新利用的重要工具.
人工智能,机器学习和KG的整合代表了加速确定现有药物的新治疗用途的重要前沿.
基于KG的方法的进一步开发和应用对于优化药物发现管道至关重要.

关键词:

深度学习是一种深度学习.药物重新定位药物重新定位图表卷积网络的图表卷积网络.知识图表知识图表机器学习是机器学习.

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