Learning a Patent-Informed Biomedical Knowledge Graph Reveals
  Technological Potential of Drug Repositioning Candidates
Yongseung Jegal Jaewoong Choi Jiho Lee Ki-Su Park Seyoung Lee Janghyeok Yoon
Drug repositioning-a promising strategy for discovering new therapeutic uses
for existing drugs-has been increasingly explored in the computational science
literature using biomedical databases. However, the technological potential of
drug repositioning candidates has often been overlooked. This study presents a
novel protocol to comprehensively analyse various sources such as
pharmaceutical patents and biomedical databases, and identify drug
repositioning candidates with both technological potential and scientific
evidence. To this end, first, we constructed a scientific biomedical knowledge
graph (s-BKG) comprising relationships between drugs, diseases, and genes
derived from biomedical databases. Our protocol involves identifying drugs that
exhibit limited association with the target disease but are closely located in
the s-BKG, as potential drug candidates. We constructed a patent-informed
biomedical knowledge graph (p-BKG) by adding pharmaceutical patent information.
Finally, we developed a graph embedding protocol to ascertain the structure of
the p-BKG, thereby calculating the relevance scores of those candidates with
target disease-related patents to evaluate their technological potential. Our
case study on Alzheimer's disease demonstrates its efficacy and feasibility,
while the quantitative outcomes and systematic methods are expected to bridge
the gap between computational discoveries and successful market applications in
drug repositioning research.