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.

Contents

• Introduction
• Existing Biomedical Knowledge Graphs
• Inference on Knowledge Graphs
• Conventional KG inference techniques
• GNN-based KG inference techniques
• KG-based hypothesis generation in computational drug development
• A machine learning framework for KG-based drug repurposing
• Application of KG-based drug repurposing in COVID-19
• Future directions
• KG quality control
• Scalable inference
• Coupling KGs with other biomedical data

Citation