Machine learning, particularly graph learning, is gaining increasing recognition for its transformative impact across various fields. One such promising application is in the realm of molecule design and discovery, notably within the pharmaceutical industry. Our survey offers a comprehensive overview of state-of-the-art methods in molecule design, particularly focusing on \emph{de novo} drug design, which incorporates (deep) graph learning techniques. We categorize these methods into three distinct groups: \emph{i)} \emph{all-at-once}, \emph{ii)} \emph{fragment-based}, and \emph{iii)} \emph{node-by-node}. Additionally, we introduce some key public datasets and outline the commonly used evaluation metrics for both the generation and optimization of molecules. In the end, we discuss the existing challenges in this field and suggest potential directions for future research.

翻译：机器学习，特别是图学习，正因其在各领域的变革性影响而日益受到认可。其中一个具有前景的应用是分子设计与发现领域，尤其是在制药行业中。本综述全面概述了分子设计领域的最先进方法，特别聚焦于结合（深度）图学习技术的*从头药物设计*。我们将这些方法分为三个不同类别：*i)* *一次性生成*、*ii)* *基于片段*和*iii)* *逐节点生成*。此外，我们介绍了一些关键公共数据集，并概述了分子生成与优化中常用的评估指标。最后，我们讨论了该领域现有挑战，并提出了未来研究的潜在方向。