Accurately predicting protein–ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

中文翻译：

---

计算预测蛋白质配体复合物中的结合亲和力：基于自由能的模拟和基于机器学习的评分函数。

准确预测蛋白质-配体结合亲和力可以大大促进药物发现过程，但这仍然是一个难题。为了应对这一挑战，人们提出了许多计算方法。在这些方法中，基于自由能的模拟和基于机器学习的评分函数可以潜在地提供准确的预测。在本文中，我们回顾了这两类方法，遵循一些基于自由能的模拟的热力学循环和基于机器学习的评分函数的特征表示分类法。还审查了最近的基于深度学习的预测，其中通常提取特征表示的层次结构。对两类方法的优缺点以及未来的改进方向进行了比较讨论。