ConvAHKG:基于行动的混合知识图,采用双通道卷积方法用于药物重定向
Marzieh Khodadadi AghGhaleh1,2, Rooholah Abedian3, Reza Zarghami2
1Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
Scientific reports
|February 6, 2026
概括
ConvAHKG是一种新的知识图方法,通过预测药物与疾病的关联来增强药物重定向. 它确定了非小细胞肺癌等疾病的潜在新疗法,提高了药物发现效率.
科学领域:
- 计算生物学和生物信息学
- 药物的发现和开发.
- 医学中的人工智能.
背景情况:
- 药物再利用加速了对现有药物的新疗法应用的识别,减少了开发时间和成本.
- 预测药物与疾病的关联对于有效的药物再利用至关重要,但由于数据的复杂性和阶级不平衡,它面临着挑战.
研究的目的:
- 提出ConvAHKG,一种基于行动的混合知识图方法,用于更好地预测药物和疾病的关联.
- 为了利用生物关系,并整合药物/疾病特征,为一个全面的药物重定向框架.
- 解决药物疾病数据集中的阶级不平衡问题,提高预测性能.
主要方法:
- 使用Word2Vec嵌入式来捕捉生物实体 (药物,蛋白质,疾病) 之间的语义相似性.
- 引入了一种新的双通道1D卷积神经网络 (IDC_Conv1D),用于分类药物疾病对.
- 实现了一个加权的二进制交叉损失函数,以减轻类不平衡问题.
主要成果:
- 与最先进的模型相比,ConvAHKG实现了更高的性能,曲线下的面积 (AUC) 为0.9836和精度回忆曲线下的面积 (AUPRC) 为0.9686.6.
- 该框架成功地确定了非小细胞肺癌 (NSCLC) 的有希望的治疗候选者,包括Trastuzumab.
- 分子对接分析支持预测化合物的潜力作为一种新的NSCLC治疗选择.
结论:
- ConvAHKG为药物重新定位和预测药物疾病关联提供了一种强大而有效的方法.
- 该方法显示了识别新型治疗策略的巨大潜力,特别是对于像NSCLC这样的复杂疾病.
- 该研究提供了一个经过验证的计算框架和开源资源,用于推进药物发现工作.
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