可以通过基于路径的知识图表完成可解释的药物重用
Ana Jiménez1, María José Merino1, Juan Parras2
1Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Avda. Complutense, 30, 28040, Madrid, Spain.
Scientific reports
|July 18, 2024
概括
我们开发了XG4Repo,这是一个可解释的人工智能框架,用于药物重新使用. 它使用生物医学知识图来识别现有药物的新用途,提供可解释的验证路径.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算药理学计算药理学
- 人工智能在药物发现中的作用
背景情况:
- 药物再利用加速了对现有药物的新疗法应用的发现,节省了时间和成本.
- 人工智能 (AI) 和知识图 (KGs) 可以处理庞大的数据集,以识别药物重用候选人.
- 可解释性对于验证人工智能驱动的预测在药物重用中至关重要.
研究的目的:
- 建议使用KG.用可解释的图表完成方法的一般架构.
- 设计和介绍XG4Repo,这是一个新的框架,用于可解释的药物重用.
- 为药物重用预测提供可解释的解释.
主要方法:
- 开发了XG4Repo,一个利用生物医学KG连接的框架,将化合物与疾病联系起来.
- 实现了多类型,多长度元路径的自动生成和优化.
- 专注于基于连接化合物到疾病的途径生成解释,包括中间生物医学实体.
主要成果:
- 通过可解释的路径,XG4Repo成功地将化合物与疾病联系起来.
- 该框架利用基因,通路,副作用和解剖学等节点进行全面的解释.
- 通过分析Epirubicin,Paclitaxel和Predinisone重定位的三个用例来证明它的实用性.
结论:
- XG4Repo增强了人工智能驱动的药物重定向预测的解释性.
- 该框架促进了专家验证和进一步研究药物重新用途.
- 可解释的AI方法对于推进高效可靠的药物发现管道至关重要.
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