ABSTRACT
Network pharmacology, as a new developmental direction of drug discovery, was generating attention of more and more researchers. The key problem in drug discovery was how to identify the new interactions between drugs and target proteins. Prediction of new interaction was made to find potential targets based on the predicting model constructed by the known drug-protein interactions. According to the deficiencies of existing predicting algorithm based bipartite graph, a supervised learning integration method of bipartite graph was proposed in this paper. Firstly, the bipartite graph network was constructed based on the known interactions between drugs and target proteins. Secondly, the evaluation model for association between drugs and target proteins was created. Thirdly, the model was used to predict the new interactions between drugs and target proteins and confirm the new predicted targets. On the testing dataset, our method performed much better than three other predicting methods. The proposed method integrated chemical space, therapeutic space and genomic space, constructed the interaction network of drugs and target proteins, created the evaluation model and predicted the new interactions with good performance.
Subject(s)
Algorithms , Drug Delivery Systems , Methods , Drug Discovery , Methods , Genomics , Methods , Models, Theoretical , Pharmaceutical Preparations , Metabolism , Protein Binding , Protein Interaction Mapping , Methods , Proteins , Genetics , MetabolismABSTRACT
Drug targets discovery is one of the most important elements in new drug development, and a variety of methods have been developed recently from this point of view. This paper proposed a network-based local and global consistency for cardiovascular genes identification. Results were evaluated through the widely used database HPRD and DrugBank. Results showed that our algorithm can give reasonable candidate targets set. The method in this paper could be an impressive solution for targets searching.