English(EN) On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints

通过ECFP预训练增强用于药物发现的GNN

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-11 15:30

研究人员开发了一种新策略，以增强图神经网络（GNN）在药物发现任务（如定量结构-活性关系（QSAR））中的应用。该方法涉及预训练GNN以预测扩展连接指纹（ECFP），这是一种经典的分子特征化方法。预训练的GNN在多个基准测试中表现出统计学上的显著改进，尤其是在分布外拆分方面。然而，其有效性因数据集异质性和终点复杂性而异，在某些情况下，在分布外设置中表现不佳。 AI

影响增强了GNN在药物发现中的性能，可能加速QSAR分析和药物开发。

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在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Garrett M. Morris · 2026-05-11 15:30

关于通过在扩展连接指纹上进行预训练来改进QSAR图神经网络的研究

Molecular Graph Neural Networks (GNNs) are increasingly common in drug discovery, particularly for Quantitative Structure-Activity Relationship (QSAR) studies; yet, their superiority compared to classical molecular featurisation approaches is disputed. We report a general strateg…

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关于通过在扩展连接指纹上进行预训练来改进QSAR图神经网络的研究

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