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. A thorough review of related KaGML works, collected following a carefully designed search methodology, are organised 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.

翻译：将人工智能（AI）整合到药物发现领域一直是跨学科科学研究中日益增长的方向。然而，传统AI模型在处理复杂生物医学结构（如2D或3D蛋白质和分子结构）以及为输出提供解释方面存在严重局限性，这阻碍了其实际应用。近年来，图机器学习（GML）因其在建模图结构生物医学数据以及探究其性质和功能关系方面的卓越能力而受到广泛关注。尽管付出了大量努力，GML方法仍存在若干不足，例如处理监督稀疏性、在学习和推理过程中提供可解释性的能力有限，以及无法有效利用相关领域知识。为此，近期研究提出将外部生物医学知识整合到GML流程中，以在训练样本有限的情况下实现更精确和可解释的药物发现。然而，这一新兴研究方向尚缺乏系统性定义。本综述全面概述了长期存在的药物发现原则，提供了图结构数据和知识数据库的基础概念与前沿技术，并正式总结了面向药物发现的知识增强图机器学习（KaGML）。按照精心设计的搜索方法收集的相关KaGML研究工作，依据新定义的分类法被系统地分为四类。为促进这一迅速兴起领域的研究，我们还分享了有助于智能药物发现的实用资源，并深入讨论了未来发展的潜在方向。