Inferring Potential Drug-Target Interactions using Multiple Similarities and Network Consistency Projection

Developing new drugs is time-consuming, labor-intensive and costly. Identifying new targets for existing drugs can help to discover new potential therapeutic uses of old drugs and reduce the cost of drug development. Drug-target interactions are usually inferred by searching for similar drugs and targets. Various biomedical databases have been established currently, which provide effective data for predicting drug-target interactions. We proposed a novel computational model for discovering Drug-Target Interactions using Network consistency project (DTIN). The Gaussian kernel similarity of drugs and targets were derived from known drug-target interactions by Gaussian kernel function, thus DTIN incorporated six types of similarities, including drug chemical structure similarity, drug ATC similarity, drug Gaussian kernel similarity, target sequence similarity, target function similarity, and target Gaussian kernel similarity. We used logistic regression to process the integrated similarity and predicted scores of interacting drug-target pairs by network consistency projection. Five-fold cross-validation was implemented on a benchmark dataset, and the computational results demonstrated that DTIN was effective and outperformed two advanced models.