SMPR:一种结构增强的多模式药物 - - 疾病预测模型,用于药物重新定位和冷启动
Xin Dong1,2,3, Rui Miao4, Suyan Zhang5
1Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macao, 999078, China.
Journal of cheminformatics
|October 15, 2025
概括
本研究介绍了用于药物重新定位的结构增强多模式关系预测 (SMRP) 模型. SMRP有效地预测了新的药物疾病关系,并利用结构信息解决了使用新药数据的挑战.
科学领域:
- 计算机化药物发现.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 药物重新定位加速治疗发展,但受现有数据和药物结构信息未充分利用的限制.
- 现有的模型往往会与现有技术扎.
- 寒冷的开始开始.
- 这是一个很大的问题,特别是对于缺乏广泛数据的新药.
研究的目的:
- 提出一种新的结构增强多式联络预测 (SMRP) 模型,以改进药物疾病关系预测.
- 利用药物结构信息 (SMILES) 和多式联络数据来提高预测准确度.
- 开发一种实用的解决方案,用于药物冷启动问题在重新定位.
主要方法:
- 使用MOL2VEC从SMILES结构生成药物嵌入式表示.
- 使用异质网络图表神经网络来学习疾病嵌入式表示.
- 构建了一个药物-疾病关系矩阵,并根据结构相似性开发了一个冷启动接口.
主要成果:
- 在药物重新定位方面取得了高性能,AUC为99%,ACUPR为61%.
- 证明了强大的冷启动能力,AUC为80%,召回超过70%.
- 案例分析证实了SMRP模型的实际价值和结构性改进.
结论:
- 通过整合结构信息和解决冷启动问题,SMRP模型在药物重新定位方面取得了重大进展.
- 该模型的用户友好界面和本地部署增强了其对研究人员的实际应用性.
- SMRP对加速发现现有药物和新药物的新疗法应用具有前景.
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