Predicting how a drug-like molecule binds to a specific protein target is a
core problem in drug discovery. An extremely fast computational binding method
would enable key applications such as fast virtual screening or drug
engineering. Existing methods are computationally expensive as they rely on
heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps.
We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep
learning model performing direct-shot prediction of both i) the receptor
binding location (blind docking) and ii) the ligand's bound pose and
orientation. EquiBind achieves significant speed-ups and better quality
compared to traditional and recent baselines. Further, we show extra
improvements when coupling it with existing fine-tuning techniques at the cost
of increased running time. Finally, we propose a novel and fast fine-tuning
model that adjusts torsion angles of a ligand's rotatable bonds based on
closed-form global minima of the von Mises angular distance to a given input
atomic point cloud, avoiding previous expensive differential evolution
strategies for energy minimization.

本研究提出了一种基于 deep learning 的药物分子与蛋白质结合位置的直接预测方法，可应用于快速虚拟筛查或制药工程，与传统方法相比，具有更高的速度和更好的质量，并提出了一种快速的微调模型来解决以往能量最小化所采用的昂贵差分进化策略。