Molecular docking is critical to structure-based virtual screening, yet the
throughput of such workflows is limited by the expensive optimization of
scoring functions involved in most docking algorithms. We explore how machine
learning can accelerate this process by learning a scoring function with a
functional form that allows for more rapid optimization. Specifically, we
define the scoring function to be the cross-correlation of multi-channel ligand
and protein scalar fields parameterized by equivariant graph neural networks,
enabling rapid optimization over rigid-body degrees of freedom with fast
Fourier transforms. The runtime of our approach can be amortized at several
levels of abstraction, and is particularly favorable for virtual screening
settings with a common binding pocket. We benchmark our scoring functions on
two simplified docking-related tasks: decoy pose scoring and rigid conformer
docking. Our method attains similar but faster performance on crystal
structures compared to the widely-used Vina and Gnina scoring functions, and is
more robust on computationally predicted structures. Code is available at
this https URL

分子对接是基于结构的虚拟筛选的关键，本研究利用机器学习加速该过程，通过学习一种具有更快优化速度的函数形式评分函数，使用图神经网络参数化的多通道配体和蛋白质标量场的互相关作为评分函数，实现快速优化刚体自由度，并在常见结合口袋的虚拟筛选设置中表现良好。