BDN-DDI:用于药物相互作用预测的双线双视图表示学习框架
Guoquan Ning1, Yuping Sun1, Jie Ling1
1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China.
Computers in biology and medicine
|August 21, 2023
概括
这项研究引入了BDN-DDI,这是通过分析药物分子中的原子相互作用来预测药物相互作用 (DDI) 的新框架. BDN-DDI显著提高了预测准确性,特别是在具有挑战性的冷启动场景中.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 人工智能在医学中的应用
背景情况:
- 药物相互作用 (DDI) 可以引起不良反应或降低药物的疗效.
- 准确的DDI预测对于患者安全和有效的药物治疗至关重要.
- 现有的方法往往忽略了药物基结构中详细的原子相互作用,限制了预测性能.
研究的目的:
- 提出一个新的框架,BDN-DDI,用于增强药物相互作用预测.
- 通过结合详细的原子级信息来解决当前方法的局限性.
- 提高DDI预测模型的准确性和稳定性.
主要方法:
- 开发了BDN-DDI,一个双线双视图表示学习框架.
- 利用堆叠的BDN块在分子水平上进行特征提取.
- 用于药物基结构嵌入的内层和间层学习.
- 包含一个解码器来预测DDI事件.
主要成果:
- 在热启动任务中实现了超过99%的AUROC值.
- 在冷启动任务中平均比最先进的方法高出3.4%.
- 通过案例研究可视化来证明有效性.
结论:
- 在DDI预测准确度方面,BDN-DDI提供了显著的进步.
- 该框架捕捉原子相互作用的能力增强了其预测能力.
- BDN-DDI显示出对现实世界的临床应用有很大的潜力.
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