Integrating multi-source drug information to cluster drug-drug interaction network

Characterizing drug-drug interactions is important to improve efficacy and/or slow down the evolution of antimicrobial resistance. Experimental methods are both time-consuming and laborious for characterizing drug-drug interactions. In recent years, many computational methods have been proposed to explore drug-drug interactions. However, these methods failed to effectively integrate multi-source drug information. In this study, we propose a similarity matrix fusion (SMF) method to integrate four drug information (i.e., structural similarity, pharmaceutical similarity, phenotypic similarity and therapeutic similarity). SMF combined with t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering algorithm can effectively identify drug groups and group-group interactions are almost monochromatic (purely synergetic or purely antagonistic). To evaluate clustering quality (i.e., monochromaticity), two measures (edge purity and edge normalized mutual information) are proposed, and SMF showed the best performance. In addition, clustered drug-drug interaction network can also be used to predict new drug-drug interactions (accuracy = 0.741). Overall, SMF provides a comprehensive view to understand drug groups and group-group interactions.