GNN-based Biomedical Knowledge Graph Mining in Drug Development

Abstract

Drug discovery and development (D³) is an extremely expensive and time consuming process. It takes tens of years and billions of dollars to make a drug successfully on the market from scratch, which makes this process highly inefficient when facing emergencies such as COVID-19. At the same time, a huge amount of knowledge and experience has been accumulated during the D³ process during the past decades. These knowledge are usually encoded in guidelines or biomedical literature, which provides an important resource containing insights that can be informative of the future D³ process. Knowledge graph (KG) is an effective way of organizing the useful information in those literature so that they can be retrieved efficiently. It also bridges the heterogeneous biomedical concepts that are involved in the D³ process. In this chapter we will review the existing biomedical KG and introduce how GNN techniques can facilitate the D³ process on the KG. We will also introduce two case studies on Parkinson’s disease and COVID-19, and point out future directions.