计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 127-134.doi: 10.11896/jsjkx.200700068
高创1, 李建华1,2, 季秀怡1, 朱程龙1, 李诗良2, 李洪林2
GAO Chuang1, LI Jian-hua1,2, JI Xiu-yi1, ZHU Cheng-long1, LI Shi-liang2, LI Hong-lin2
摘要： 药物-靶标作用关系预测在药物研发以及药物重定位中扮演着重要角色,但现有的机器学习方法在正负样本高度不平衡的数据上仍存在预测能力不足的问题。为此,提出一种基于图卷积神经网络的药物靶标作用关系预测方法。该方法首先构造一个结合多种药物(靶标)相关信息的异质信息网络,然后采用图卷积神经网络在此异质信息网络上学习得到能精确表达每个节点拓扑特征及邻居特征信息的低维向量表征,最后利用这些向量信息通过向量空间投影预测节点间概率的评分。在DrugBank_FDA和Yammanishi_08数据集上进行的药物-靶标作用关系预测的对比实验中,所提方法的AUPR(Area Under the Precision-Recall Curve)值都优于其他4种方法,并且在较大型数据集上也有较好的表现。实验结果表明,所提方法提高了样本高度不平衡时的药物-靶标作用关系预测性能;同时在生物药物数据库上的实验也验证了所提方法所发现的未知药物-靶标作用关系的有效性。
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