Exploration and analysis of drug modes of action through feature integration.

Abstract

Identifying drug modes of action (MoA) is of paramount importance for having a good grasp of drug indications in clinical tests. Anticipating MoA can help to discover new uses for approved drugs. Here we first used a drug-set enrichment analysis method to discover significant biological activities in every mode of action category. Then, we proposed a new computational model, a probability ensemble approach based on Bayesian network theory, which integrated chemical, therapeutic, genomic and phenotypic properties of over a thousand of FDA approved drugs to assist with the prediction of MoA. 10-fold cross validation tests demonstrate that this method can achieve better performances than four other methods with the area under the receiver operating characteristic (ROC) curves. Finally, we further conducted a large-scale prediction for drug-MoA pairs. Using the Cardiovascular Agents category as an example, several predicted drug-MoA pairs were supported by literature resources.