Massively Parallel Processing Database for Sequence and Graph Data Structures Applied to Rapid-Response Drug Repurposing

In this paper, we present the application of a massively parallel-processing graph database for rapid-response drug repurposing. The novelty of our approach is that the scalable graph database is able to host a knowledge graph of medically relevant facts integrated from multiple knowledge sources and also act as a computational engine capable of in-database protein sequence analytics. We demonstrate the performance of the graph database on a real-world use-case to hypothesize cures for COVID-19, leveraging its built-in accelerated protein-sequence matching capabilities at unprecedented scale (to simultaneously handle data size and query latency requirements for interactive research). Based on supporting evidence from medical literature, we show that results generated by computing similarity of COVID-19 virus proteins across 4 million other open-science sequences and intelligently traversing over a 150 billion facts from open-science medical knowledge produces biologically insightful results. By presenting sample queries and extending application to use-cases beyond COVID-19, we demonstrate the use and value of the novel database for hypotheses generation in reducing the time-to-insight and increasing researcher productivity with interactivity.