基于分子子结构和知识图的神经网络的药物相互作用预测的有效框架
Siqi Chen1, Ivan Semenov2, Fengyun Zhang2
1School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, 400074, China.
Computers in biology and medicine
|January 10, 2024
概括
预测药物相互作用 (DDI) 对患者安全至关重要. 一个新的AI框架,MSKG-DDI,通过整合药物化学结构和知识图表,有效地预测DDI,优于现有方法.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 药物相互作用 (DDI) 在药物研究中至关重要,影响疗效并引起不良反应.
- 目前用于DDI预测的AI模型往往忽视了药物分子结构或药物之间的关系.
- 对医生,患者和研究人员来说,识别潜在的DDI至关重要.
研究的目的:
- 开发一个先进的AI框架来预测药物相互作用 (DDI).
- 通过整合化学结构和知识图信息来捕捉多式药物特征.
- 提高DDI预测模型的准确性和范围.
主要方法:
- 拟议的MSKG-DDI是一个两部分框架,集成药物化学结构图和药物知识图.
- 利用多式融合神经层来结合药物表征.
- 对二进制类,多类和多标签DDI预测任务的评估性能.
主要成果:
- 在现实数据集上,MSKG-DDI显著超过了最先进的模型.
- 该框架在传导和感应环境中表现出卓越的性能.
- 废弃分析证实了拟议组件的实际实用性和有效性.
结论:
- MSKG-DDI框架为DDI预测提供了一种新有效的方法.
- 整合多模式药物信息,包括分子结构和知识图表,提高了预测准确性.
- 这项研究强调了在计算药物研究中全面特征表示的重要性.
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