Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.

用药物小分子（SM）靶向microRNA（miRNA）是许多人类复杂疾病的新治疗方法。毫不奇怪，潜在的miRNA-SM关联的识别有助于医学研究领域的药物工程和疾病治疗。在本文中，我们通过在异构网络上结合已知的miRNA-SM关联，集成的miRNA相似性，在异构网络上实施基于路径的HeteSim的测量方法，开发了一种基于HeteSim的SM-miRNA关联预测（HSSMMA）推断的新计算模型。并整合了SM相似性。通过考虑异构网络中从SM到miRNA的路径，该模型可以捕获每个路径下的语义信息，并基于所有考虑的路径来预测潜在的miRNA-SM关联。我们在全球演出 基于已知miRNA-SM关联的数据集，miRNA固定的局部和SM固定的局部留一法交叉验证（LOOCV）以及5倍交叉验证，以评估我们方法的预测性能。结果表明，基于数据集1，HSSMMA在接收器工作特性（ROC）曲线（AUC）下获得了相应的区域，分别为0.9913、0.9902、0.7989和0.9910±0.0004，而基于AUC的接收器分别为0.7401、0.8466、0.6149和0.7451±0.0054分别在数据集2上。在案例研究中，已发表的文献证实了预测的潜在miRNA-SM关联的前10位中的2位和前50位中的13位。我们进一步实施了案例研究，以测试HSSMMA对于没有任何已知相关miRNA的新型SM是否有效。