PRID:使用RWR用于药物之间的相互作用的预测模型
Jiwon Seo1, Hyein Jung1, Younhee Ko1
1Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin 17035, Gyeonggi-do, Republic of Korea.
Pharmaceutics
|October 28, 2023
概括
这项研究介绍了PRID,这是一种深度学习模型,使用化学结构和蛋白质相互作用来预测药物相互作用 (DDI). PRID有效地识别了已知和潜在的DDI,提高了患者的安全.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 药物相互作用 (DDI) 可以引起意想不到的药理学效应,使多药学复杂化.
- 目前的DDI鉴定依赖于临床经验,缺乏安全联合处方的标准化数据库.
- 现有的DDI预测计算方法由于功能可用性不完整和无法捕捉复杂的病理机制而存在局限性.
研究的目的:
- 开发一种新的深度学习模型,用于预测药物相互作用 (DDI).
- 整合化学结构相似性和蛋白质-蛋白质相互作用 (PPI) 数据,以提高DDI预测.
- 通过准确识别潜在的DDI来改善患者安全和治疗策略.
主要方法:
- 开发了一个深度学习模型,PRID,利用化学结构相似性和药物结合蛋白 (CTET) 信息.
- 随机步行与重启 (RWR) 算法用于在PPI网络 (STRING数据库) 中传播CTET蛋白.
- 这种方法结合了CTET蛋白和与疾病相关的基因之间的隐藏生物机制.
主要成果:
- PRID模型成功预测了已知的药物相互作用,包括涉及药物的药物相互作用.
- CTET蛋白的RWR传播有效地捕获了与DDI相关的间接共同调节的生物机制.
- 该研究证明了PRID在预测已知和新药组合方面的有效性.
结论:
- 通过整合各种生物和化学数据,PRID提供了一种可靠的方法来预测药物相互作用.
- 该模型揭示隐藏的生物机制的能力增强了对DDI因果关系的理解.
- PRID有可能识别新的DDI并指导更安全的多药实践.
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