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Updated: May 20, 2025

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基于多种药物特征预测药物不良反应的融合深度学习模型
Qing Ou1, Xikun Jiang1, Zhetong Guo1
1School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.
Life (Basel, Switzerland)
|March 27, 2025
概括
这项研究引入了一种人工智能驱动的深度学习模型,用于预测药物不良反应 (ADRs) 在药物发现的早期. 这种先进的模型整合了多种药物特征,大大提高了预测准确性和稳定性,从而提高了药物安全性.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 药物不良反应 (ADR) 对患者安全构成重大风险,并增加医疗保健成本.
- 以前的ADR预测模型经常使用有限的数据尺寸,并专注于每种药物的单个ADR.
- 整合不同的药物特征为ADR预测提供了更全面的方法.
研究的目的:
- 开发一种人工智能驱动的预测器,用于在药物发现过程中早期识别ADR.
- 通过使用深度学习模型融合多种药物特征来提高ADR预测的准确性.
- 通过实现多标签预测,解决仅预测单个ADR的局限性.
主要方法:
- 开发了一个深度学习模型,整合了四个模块:1D/2D药物分子结构,1D/2D药物分子结构,药物蛋白相互作用和药物相似性.
- 采用融合模型将这些特征结合起来,精确地预测ADR概率.
- 利用基准和LIU的数据集进行模型评估和与最先进的方法进行比较.
主要成果:
- 在基准数据集上实现了0.7002的ROC-AUC,0.6619的AUPR和0.6330的F1得分.
- 与传统的多标签分类器相比,AUPR显著改善 (64.02%至66.19%).
- 在LIU的数据集上,超越了最先进的方法,AUPR从34.65%增加到68.82%.
结论:
- 开发的AI模型通过整合全面的药物信息,准确预测ADR概率.
- 这种方法为加强新药开发和临床使用中的药物安全监测提供了重要的价值.
- 该模型在识别潜在的不良药物反应方面表现出卓越的准确性和稳定性.
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