Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. The binding affinity, which refers to the strength of this interaction, is central to many important problems in bioinformatics such as drug design. An extensive amount of work has been devoted to predicting binding affinity over the past decades due to its significance. In this paper, we review all significant recent works, focusing on the methods, features, and benchmark datasets. We have observed a rising trend in the use of traditional machine learning and deep learning models for predicting binding affinity, accompanied by an increasing amount of data on proteins and small drug-like molecules. While prediction results are constantly improving, we also identify several open questions and potential directions that remain unexplored in the field. This paper could serve as an excellent starting point for machine learning researchers who wish to engage in the study of binding affinity, or for anyone with general interests in machine learning, drug discovery, and bioinformatics.

本研究针对蛋白质-配体结合亲和力预测的关键问题，回顾了传统与机器学习模型在该领域的应用进展。通过总结现有的方法和数据集，指出了该领域仍存在的未解问题及未来可能的研究方向。研究的结果为机器学习研究人员和药物发现领域的从业者提供了重要的参考和启示。

结合亲和力预测：从传统方法到基于机器学习的方法