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

Transferable Graph Neural Fingerprint Models for Quick Response to  Future Bio-Threats

Proteins are fundamental biological entities mediating key roles in cellular
function and disease. This paper introduces a multi-scale graph construction of
a protein -- HoloProt -- connecting surface to structure and sequence. The
surface captures coarser details of the protein, while sequence as primary
component and structure -- comprising secondary and tertiary components --
capture finer details. Our graph encoder then learns a multi-scale
representation by allowing each level to integrate the encoding from level(s)
below with the graph at that level. We test the learned representation on
different tasks, (i.) ligand binding affinity (regression), and (ii.) protein
function prediction (classification). On the regression task, contrary to
previous methods, our model performs consistently and reliably across different
dataset splits, outperforming all baselines on most splits. On the
classification task, it achieves a performance close to the top-performing
model while using 10x fewer parameters. To improve the memory efficiency of our
construction, we segment the multiplex protein surface manifold into molecular
superpixels and substitute the surface with these superpixels at little to no
performance loss.

本文提出了一种多尺度图构建方法 ——HoloProt，可以连接蛋白表面、结构和序列，并在不同任务上进行了测试，包括配基结合亲和力回归和蛋白质功能预测分类。该模型具有良好的稳定性和可靠性，并且在减少参数数量的情况下可以获得与现有模型相当的性能。