LCM-DS: A novel approach of predicting drug-drug interactions for new drugs via Dempster-Shafer theory of evidence

Abstract

There is an urgent need to discover or predict DDIs, which would cause serious adverse drug reactions. However, preclinical detection of DDIs bear high cost. Similarity-based computational approaches can be the assistance of experimental approaches. Utilizing pre-market drug similarities, they are able to predict DDIs on a large scale. However, they neglect the topological structure among DDIs and non-DDIs and have a burden of slow training and much memory. Or, they bear the bias that the pairs between a newly-given drug and the drugs having many DDIs tend to obtain high ranks. More importantly, they lack an effective combination of multiple predictions. To address these issues, we develop a local classification-based model (LCM), which has the advantages of faster training, less memory requirement as well as no that bias. We further design a novel supervised algorithm of fusion based on Dempster-Shafer (DS) theory of evidence for combine multiple predictions. Finally, the experiments demonstrate that our LCM-DS is significantly superior to three state-of-the-art approaches and outperforms both individual LCMs and classical fusion algorithms.