  The integration of Artificial Intelligence (AI) into the field of drug
discovery has been a growing area of interdisciplinary scientific research.
However, conventional AI models are heavily limited in handling complex
biomedical structures (such as 2D or 3D protein and molecule structures) and
providing interpretations for outputs, which hinders their practical
application. As of late, Graph Machine Learning (GML) has gained considerable
attention for its exceptional ability to model graph-structured biomedical data
and investigate their properties and functional relationships. Despite
extensive efforts, GML methods still suffer from several deficiencies, such as
the limited ability to handle supervision sparsity and provide interpretability
in learning and inference processes, and their ineffectiveness in utilising
relevant domain knowledge. In response, recent studies have proposed
integrating external biomedical knowledge into the GML pipeline to realise more
precise and interpretable drug discovery with limited training instances.
However, a systematic definition for this burgeoning research direction is yet
to be established. This survey presents a comprehensive overview of
long-standing drug discovery principles, provides the foundational concepts and
cutting-edge techniques for graph-structured data and knowledge databases, and
formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug
discovery. we propose a thorough review of related KaGML works, collected
following a carefully designed search methodology, and organise them into four
categories following a novel-defined taxonomy. To facilitate research in this
promptly emerging field, we also share collected practical resources that are
valuable for intelligent drug discovery and provide an in-depth discussion of
the potential avenues for future advancements.
