Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically, we first introduce the features of 2D and 3D molecular graphs. Then we summarize and categorize MRL methods into three groups based on their input. Furthermore, we discuss some typical chemical applications supported by MRL. To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Finally, we share our thoughts on future research directions.

翻译：分子表示学习（MRL）是建立机器学习与化学科学之间联系的关键步骤。具体而言，它通过将分子编码为保留分子结构与特征的数值向量，进而可执行下游任务（如性质预测）。近年来，MRL取得了显著进展，尤其是基于深度分子图学习的方法取得了重大突破。在本综述中，我们系统回顾了这些基于图的分子表示技术，特别是融入化学领域知识的方法。具体而言，我们首先介绍了二维和三维分子图的特征，然后根据输入形式将MRL方法归纳为三类。此外，我们讨论了MRL支持的一些典型化学应用场景。为促进这一快速发展领域的研究，本文还列出了相关基准测试与常用数据集。最后，我们分享了对未来研究方向的思考。