基于链路预测的协同药物组合推荐研究:面向疾病并发症诊疗

[Purpose/significance] Compared with single drug, drug combination has many advantages in clinical treatment. But the growth of drug quantity brings difficulties to drug combination screening experiment. Therefore, it is of great significance to design an effective prediction method to recommend drug combination which is more likely to produce synergistic effect for pharmaceutical staff, so as to improve the screening efficiency.[Method/process] For the need of concurrent diseases treatment, proposed a drug combination recommendation model based on link prediction, and used the SAO semantic mining to identify the complications in medical literature. On this basis, we used the medical database to build the heterogeneous "disease-drug-target" network, and introduced link prediction to evaluate the similarity of drug action mechanism, and predicted which drug combinations were more likely to have synergistic effect. Based on the prediction results, recommended a combination of drugs for a certain disease or a pair of complications.[Result/conclusion] The empirical analysis of intestinal disease data verified the practicality and efficiency of the model.