Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey

Abstract

Artificial Intelligence has become integral to intelligent drug discovery, with Graph Machine Learning (GML) emerging as a powerful structure-based method for modelling graph-structured biomedical data and investigating their properties. However, GML faces challenges such as limited interpretability and heavy dependency on abundant high-quality training data. On the other hand, knowledge-based methods leverage biomedical knowledge databases, e.g. , Knowledge Graphs (KGs), to explore unknown knowledge. Nevertheless, KG construction is resource-intensive and often neglects crucial structural information in biomedical data. In response, recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery with scarce training data. Nevertheless, a systematic definition for this burgeoning research direction is yet to be established. This survey formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug discovery and organises collected KaGML works into four categories following a novel-defined taxonomy. We also present a comprehensive overview of long-standing drug discovery principles and provide the foundational concepts and cutting-edge techniques for graph-structured data and knowledge databases. To facilitate research in this promptly emerging field, we share collected practical resources that are valuable for intelligent drug discovery and provide an in-depth discussion of the potential avenues for future advancements.