Fast screening of drug molecules based on the ligand binding affinity is an important step in the drug discovery pipeline. Graph neural fingerprint is a promising method for developing molecular docking surrogates with high throughput and great fidelity. In this study, we built a COVID-19 drug docking dataset of about 300,000 drug candidates on 23 coronavirus protein targets. With this dataset, we trained graph neural fingerprint docking models for high-throughput virtual COVID-19 drug screening. The graph neural fingerprint models yield high prediction accuracy on docking scores with the mean squared error lower than $0.21$ kcal/mol for most of the docking targets, showing significant improvement over conventional circular fingerprint methods. To make the neural fingerprints transferable for unknown targets, we also propose a transferable graph neural fingerprint method trained on multiple targets. With comparable accuracy to target-specific graph neural fingerprint models, the transferable model exhibits superb training and data efficiency. We highlight that the impact of this study extends beyond COVID-19 dataset, as our approach for fast virtual ligand screening can be easily adapted and integrated into a general machine learning-accelerated pipeline to battle future bio-threats.

基于配体结合亲和力的快速药物分子筛选是药物发现流程中的重要一步。本研究利用图神经指纹方法构建了一个包含约300,000种化合物的COVID-19药物分子对接数据集，针对23个冠状病毒蛋白靶点开展了高通量虚拟COVID-19药物筛选，并提出了一种可迁移图神经指纹方法以适用于未知的靶点，展现了出色的训练和数据效率。研究结果表明，图神经指纹模型在大部分对接靶点上的预测准确性高于常规的环形指纹方法，在平均平方误差低于0.21 kcal/mol的范围内，显著改进了对接得分的预测精度。该研究的影响不仅局限于COVID-19数据集，而且我们的快速虚拟配体筛选方法可轻松地适应和整合到未来对抗生物威胁的通用机器学习加速流程中。

可传递的图神经指纹模型对未来生物威胁做出快速响应