药物靶标作用关系预测结果评价及查询验证

Predicted Results Evaluation and Query Verification of Drug-Target Interaction

药物靶标作用关系预测是一种重要的辅助药物研发手段,而生物实验验证药物靶标作用关系耗钱耗时,因此,在数据库中查询验证预测的药物靶标作用关系是对预测方法的重要评价.基于KEGG,DrugBank,ChEMBL这3个数据库,利用爬虫获取信息的方式设计开发了药物靶标作用关系查询验证方法DTcheck(drug-target check),实现了对于提供KEGG DRUG ID及KEGG GENES ID的药物靶标对的高效查询验证功能,并利用DTcheck分别为Enzyme,IC(ion channel),GPCR(G-protein-coupled receptor),NR(nuclear receptor)四个标准数据集扩充新增药物靶标作用关系907,766,458,40对.此外,结合DTcheck查询验证,以BLM(bipartite local models)方法为例分析了预测结果的评价问题,结果表明,采用AUC(area under curve)值评价药物靶标作用关系预测方法没有Top N 评价合理,且AUC值低的BLMd方法在预测新的药物靶标作用关系时优于AUC值高的BLMmax方法.

The drug-target interaction prediction is one of the important assistant approaches in drug discovery and design, however, experimental identification and validation of potential drug-target encoded by the human genome is both costly and time-consuming. Therefore, querying and validating the predicted drug-target interaction in databases is an important assessment of prediction methods. In this paper, the query and validation method of drug-target interaction named as DTcheck (drug-target check) is developed and designed with Web spider based on KEGG, DrugBank, ChEMBL databases, which realizes efficient query and validation function for drug-target pair providing both KEGG DRUG ID and KEGG GENES ID. ID mapping function is also designed in DTcheck, which can map Uniprot ID from DrugBank and ChEMBL into KEGG GENE ID. DTcheck expands 907, 766, 458, 40 pairs of new drug-target interaction for Enzyme, IC (ion channel), GPCR (G-protein-coupled receptor), NR (nuclear receptor) standard datasets, respectively. Moreover, combined with query and validation result, the analysis of the prediction results of the BLM (bipartite local models) method shows that evaluation of Top N is more reasonable than AUC (area under curve) value for the prediction method of drug-target interaction. It also shows that the BLMd method with low AUC value is superior to the BLMmax method with high AUC value in predicting the drug-target interaction.