对抗凝血化合物的快速查与生物目标相关的不良影响,使用深度学习框架来管理心房动
Tim Dong1, Rhys Llewellyn2, Melanie Hezzell3
1Bristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol BS2 8HW, UK.
Bioengineering (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一个深度学习框架,用于预测药物不良反应,帮助临床试验成功和药物发现. 该模型有效地选潜在的副作用,改善药物安全性评估.
科学领域:
- 药理学 药理学 是一个学科.
- 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:
- 深度学习显示了药物相互作用和发现的前景.
- 关于深度学习用于选与药物相关的不良影响的研究有限.
- 副作用影响药物开发和临床试验的成功.
研究的目的:
- 开发和评估深度学习框架,用于药物不良影响的预防性查.
- 确定与心房 (AF) 管理相关的新药候选人和机制.
- 预测药物持续性和临床试验中的成功.
主要方法:
- 扩展并应用基于深度学习的框架.
- 药物向与副作用,蛋白序列和受影响的器官相关联.
- 使用副作用资源 (SIDER) 和FAERS数据集验证了模型.
主要成果:
- 深度学习模型在外部验证数据集上表现出强的表现.
- 埃诺沙巴林的高精度 (0.879) 和里瓦罗克萨班的回忆 (0.746) 获得.
- 确定了edoxaban的较强出血风险和sequoiaflavone和rivaroxaban的类似配置文件.
结论:
- 开发的框架允许对药物的不良影响进行预防性查.
- 该方法考虑了药物安全的生物基础.
- 通过预测潜在的副作用,促进最佳药物选择和开发.
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