The curse and blessing of abundance—the evolution of drug interaction databases and their impact on drug network analysis

Abstract Background Widespread bioinformatics applications such as drug repositioning or drug–drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets—we know the drug–drug or drug–target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications. Results We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug–drug and drug–target interaction networks—built with data from DrugBank versions released over the plast decade—to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug–drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug–target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves. Conclusions Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug–target interaction prediction and drug–drug interaction severity standardization.