Drug-drug interaction analysis using heterogeneous biological information network

As the number of drugs increases, more prescription choices are available for physicians, and consequently the number of drugs administered together has increased. Researchers are working on finding multi-drug prescriptions that are effective and safe. An efficient method for finding DDIs plays a crucial role in this research. In order to address the problem, we construct a heterogeneous biological information network by combining multiple different databases and interaction information. Our network includes the information about genes, proteins, pathways, drugs, side effects, targets and their interactions. We propose a metric to measure the relation strength between two nodes in the network, which is based on the weighted sum of the numbers of paths containing different interaction types. We use the metric to score DDI candidates. We found that the drugs sharing a disease are more likely to have a DDI than the drugs sharing a biomolecular target, and the metric using the weighted sum of the path numbers is effective to rank the potential DDIs. We validated the result with the PharmGKB DDI dataset and the Drugs.com drug interaction checker.