Predicting Natural Product-Drug Interactions with Knowledge Graph Embeddings.

Abstract

Natural product-drug interactions (NPDIs) occurring due to concomitant exposure to botanical products and prescription drug therapies could lead to adverse events or reduced treatment efficacy. To better understand and address potential safety concerns, researchers investigate the underlying NPDI mechanisms using in vitro and clinical studies. Given that natural products are complex mixtures of compounds that are often not well characterized, it is important to advance computational methods for novel NPDI research. Biomedical knowledge graphs (KGs) can aid in identifying potential mechanisms to support such research efforts. We evaluated the ability of several KG embedding methods to improve NPDI prediction on NP-KG, a large-scale, heterogeneous, biomedical KG. We found that the ComplEx model outperformed other KG embedding approaches in both intrinsic and extrinsic evaluations. Future work will focus on utilizing the embeddings to identify underlying mechanisms of novel, potential NPDIs.