@article{schatz_melo-filho_tropsha_chirkova_2021, title={Explaining Drug-Discovery Hypotheses Using Knowledge-Graph Patterns}, ISSN={["2639-1589"]}, DOI={10.1109/BigData52589.2021.9672006}, abstractNote={Drug discovery is an important process used by biomedical experts to identify potential treatments for diseases. In its traditional form, the process requires significant expert time and manual effort. By encoding a wealth of information about relationships between drugs and diseases, modern large-scale biomedical knowledge graphs provide excellent opportunities to accelerate drug discovery, by automating aspects of the process. One opportunity is to use explainable fact-checking tools to generate explanations for hypothesized drug-disease treatment relationships in a given knowledge graph, with a reliability score assigned to each explanation. The explanations and their scores can then be used by experts to determine which drug-disease pairs to consider for clinical trials.In our collaboration with a biomedical team, we have found that existing explainable fact-checking tools are not necessarily helpful in drug discovery, as their explanation formats and evaluation metrics do not match well the requirements of scientific discovery in the biomedical domain. To address these challenges in using fact-checking tools in drug discovery, we introduce a scalable automated approach for generating explanations that are modeled after existing biomedical concepts and supplemented with data-supported evaluation metrics. Our explanations are based on knowledge-graph patterns, which are readily understood by biomedical experts. Our experimental results suggest that our proposed metrics are accurate and useful on largescale biomedical knowledge graphs, and our explanations are understandable and reasonable to experts doing drug discovery.}, journal={2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)}, author={Schatz, Kara and Melo-Filho, Cleber and Tropsha, Alexander and Chirkova, Rada}, year={2021}, pages={3709–3716} }