While there has been discussion about noise levels in molecular docking
datasets such as PDBBind, a thorough analysis of their physical/chemical and
bioactivity noise characteristics is still lacking. PoseCheck addresses this
issue by examining molecular strain energy, molecular-protein clashes, and
interactions, but it is primarily created for $de$ $novo$ drug design. Another
important metric in molecular docking, Binding Affinity Energy, is better
assessed by the new empirical score function, AA-Score, which has demonstrated
improved performance over existing methods. To tackle these challenges, we
propose the COMPASS method, which integrates the PoseCheck and AA-Score
modules. This approach evaluates dataset noise levels and the physical/chemical
and bioactivity feasibility of docked molecules. Our analysis of the PDBBind
dataset using COMPASS reveals significant noise in the ground truth data.
Additionally, we incorporate COMPASS with the state-of-the-art molecular
docking method, DiffDock, in inference mode to achieve efficient and accurate
assessments of docked ligands. Finally, we propose a new paradigm to enhance
model performance for molecular docking through fine-tuning and discuss the
potential benefits of this approach. The source code is available publicly at
this https URL

通过对 PDBBind 数据集进行分析，我们提出了一种新的方法 COMPASS，它整合了 PoseCheck 和 AA-Score 模块，用于评估分子对接数据集的噪声水平和分子的物理 / 化学以及生物活性可行性。通过将 COMPASS 与 DiffDock 分子对接方法相结合，能够实现高效准确的配体评估，同时通过微调提高分子对接模型性能。