Molecular docking is a key computational tool utilized to predict the binding
conformations of small molecules to protein targets, which is fundamental in
the design of novel drugs. Despite recent advancements in geometric deep
learning-based approaches leading to improvements in blind docking efficiency,
these methods have encountered notable challenges, such as limited
generalization performance on unseen proteins, the inability to concurrently
address the settings of blind docking and site-specific docking, and the
frequent occurrence of physical implausibilities such as inter-molecular steric
clash. In this study, we introduce DeltaDock, a robust and versatile framework
designed for efficient molecular docking to overcome these challenges.
DeltaDock operates in a two-step process: rapid initial complex structures
sampling followed by multi-scale iterative refinement of the initial
structures. In the initial stage, to sample accurate structures with high
efficiency, we develop a ligand-dependent binding site prediction model founded
on large protein models and graph neural networks. This model is then paired
with GPU-accelerated sampling algorithms. The sampled structures are updated
using a multi-scale iterative refinement module that captures both
protein-ligand atom-atom interactions and residue-atom interactions in the
following stage. Distinct from previous geometric deep learning methods that
are conditioned on the blind docking setting, DeltaDock demonstrates superior
performance in both blind docking and site-specific docking settings.
Comprehensive experimental results reveal that DeltaDock consistently surpasses
baseline methods in terms of docking accuracy. Furthermore, it displays
remarkable generalization capabilities and proficiency for predicting
physically valid structures, thereby attesting to its robustness and
reliability in various scenarios.

DeltaDock 是一个用于高效分子对接的强大且通用的框架，它在快速采样准确结构和多尺度迭代优化初步结构两个步骤中操作，通过大型蛋白质模型和图神经网络，结合 GPU 加速采样算法，实现了盲对接和特定位点对接设置下的卓越性能和预测物理有效结构的能力。