The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking methods or even to predict all-atom protein-ligand complex structures. Most contemporary studies focus on the accuracy and physical plausibility of ligand placement to determine pose quality, often neglecting a direct assessment of the interactions observed with the protein. In this work, we demonstrate that ignoring protein-ligand interaction fingerprints can lead to overestimation of model performance, most notably in recent protein-ligand cofolding models which often fail to recapitulate key interactions.

翻译：近年来，蛋白质-配体构象预测领域取得了显著进展，基于机器学习的方法现已普遍取代经典对接方法，甚至用于预测全原子蛋白质-配体复合物结构。当前多数研究侧重于通过配体放置的准确性和物理合理性来确定构象质量，往往忽视对观测到的蛋白质-配体相互作用的直接评估。本研究表明，忽略蛋白质-配体相互作用指纹会导致模型性能的高估，这在近期常无法重现关键相互作用的蛋白质-配体共折叠模型中尤为明显。