Binding affinity prediction (BAP) using protein-ligand complex structures is crucial to computer-aided drug design, but remains a challenging problem. To achieve efficient and accurate BAP, machine-learning scoring functions (SFs) based on a wide range of descriptors have been developed. Among those descriptors, protein-ligand interaction fingerprints (IFPs) are competitive due to their simple representations, elaborate profiles of key interactions and easy collaborations with machine-learning algorithms. In this paper, we have adopted a building-block-based taxonomy to review a broad range of IFP models, and compared representative IFP-based SFs in target-specific and generic scoring tasks. Atom-pair-counts-based and substructure-based IFPs show great potential in these tasks.

使用蛋白质-配体复合结构的结合亲和力预测 (BAP) 对计算机辅助药物设计至关重要，但仍然是一个具有挑战性的问题。为了实现高效准确的 BAP，已经开发了基于各种描述符的机器学习评分函数 (SF)。在这些描述符中，蛋白质-配体相互作用指纹 (IFP) 因其简单的表示形式、关键相互作用的详细描述以及与机器学习算法的轻松协作而具有竞争力。在本文中，我们采用了基于积木的分类法来审查广泛的 IFP 模型，并在特定目标和通用评分任务中比较了具有代表性的基于 IFP 的 SF。基于原子对计数和基于子结构的 IFP 在这些任务中显示出巨大的潜力。