Molecular docking, a pivotal computational tool for drug discovery, predicts
the binding interactions between small molecules (ligands) and target proteins
(receptors). Conventional physics-based docking tools, though widely used, face
limitations in precision due to restricted conformational sampling and
imprecise scoring functions. Recent endeavors have employed deep learning
techniques to enhance docking accuracy, but their generalization remains a
concern due to limited training data. Leveraging the success of extensive and
diverse data in other domains, we introduce HelixDock, a novel approach for
site-specific molecular docking. Hundreds of millions of binding poses are
generated by traditional docking tools, encompassing diverse protein targets
and small molecules. Our deep learning-based docking model, a SE(3)-equivariant
network, is pre-trained with this large-scale dataset and then fine-tuned with
a small number of precise receptor-ligand complex structures. Comparative
analyses against physics-based and deep learning-based baseline methods
highlight HelixDock's superiority, especially on challenging test sets. Our
study elucidates the scaling laws of the pre-trained molecular docking models,
showcasing consistent improvements with increased model parameters and
pre-train data quantities. Harnessing the power of extensive and diverse
generated data holds promise for advancing AI-driven drug discovery.

通过利用大规模和多样化的生成数据，我们引入了一种用于特定位点分子对接的新方法 HelixDock，该方法基于深度学习预训练网络和小数量的精确受体 - 配体复合物结构，相较于传统基于物理和基于深度学习的基线方法，在具有挑战性的测试集上表现出卓越的优势，研究揭示了预训练分子对接模型的扩展规律，展示了随着模型参数和预训练数据数量的增加而持续改进的效果，充分利用广泛而多样化的生成数据的力量有望推进以人工智能驱动的药物发现。