通过使用图形神经网络和异质数据重新定位药物来发现隐藏的治疗指示
Adrián Ayuso-Muñoz1, Lucía Prieto-Santamaría1, Esther Ugarte-Carro1
1ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain; Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Madrid, Spain.
Artificial intelligence in medicine
|November 4, 2023
概括
BEHOR是一种先进的药物再利用模型,利用DISNET生物医学图和深度神经网络来确定现有药物的新疗法用途,降低开发成本和风险.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 药物重新用途通过确定现有药物的新用途来加速治疗发展,从而降低与新药发现相关的成本和风险.
- 基于图形的生物医学数据表示有效地捕捉复杂的关系,为高级分析模型提供了坚实的基础.
- 与生物医学图形集成的深度神经网络显示出在解决药物重定向方面的挑战方面显著的前景.
研究的目的:
- 引入BEHOR,这是REDIRECTION模型的增强代,旨在全面重新利用药物.
- 使用DISNET生物医学图表作为Behor模型的核心数据源.
- 评估Behor在预测新型药物向相互作用和治疗应用方面的疗效.
主要方法:
- 贝霍尔使用在广泛的DISNET生物医学图表上训练的深度神经网络.
- 该模型建立在之前引入的REDIRECTION模型的架构上.
- 使用RepoDB基准数据集的标准指标来评估业绩.
主要成果:
- 贝霍尔实现了接收器运行特征曲线 (AUROC) 下的面积为0.9604.
- 该模型在RepoDB测试套件上获得了0.9518的精度回忆曲线下的面积 (AUPRC).
- 分析了新的预测,以证明模型的可靠性和产生假设的潜力.
结论:
- BEHOR代表了计算药物重新定位的重大进步.
- 该模型的高性能指标表明它有可能可靠地产生药物重用假设.
- 贝霍尔准备为药物发现和开发领域做出重大贡献.
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