评估蛋白-配体结合姿态时，忽略相互作用指纹会高估模型性能。
Assessing interaction recovery of predicted protein-ligand poses
- 通过对比预测姿态与真实相互作用指纹，评估模型准确性
- 发现近期共折叠模型常遗漏关键相互作用，导致性能虚高
- 适合关注药物设计可靠性的研究人员参考
近年来，基于机器学习的蛋白-配体结合姿态预测取得显著进展，逐渐取代传统对接方法，甚至可预测全原子复合物结构。当前多数研究仅关注配体位置的准确性和物理合理性，却忽视对蛋白-配体相互作用指纹的直接评估。本文表明，忽略相互作用指纹会导致模型性能被严重高估，尤其在近期的蛋白-配体共折叠模型中，常无法重现关键相互作用，影响药物设计可靠性。
原文摘要 · Abstract (English)
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.
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