Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
复制标题
利用图注意力机制突破药物发现分子表示的界限
DOI：
10.1021/acs.jmedchem.9b00959
复制
发表时间：
2020-08-27
影响因子：
7.3
通讯作者：
Zheng, Mingyue
中科院分区：
文献类型：
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作者：
Xiong, Zhaoping;Wang, Dingyan;Zheng, Mingyue
寻找具有良好药理学、毒理学和药代动力学特性的化学物质仍然是药物发现的一个巨大挑战。深度学习为我们提供了强大的工具来构建适用于不断增加的数据量的预测模型，但这些神经网络学习的内容与人类可以理解的内容之间的差距正在扩大。此外，这种差距可能会导致不信任，并限制深度学习在实践中的应用。在这里，我们介绍了一种新的图形神经网络架构，称为Attentive FP，用于分子表示，它使用图形注意力机制从相关的药物发现数据集中学习。我们证明了Attentive FP在各种数据集上实现了最先进的预测性能，并且它所学习的内容是可解释的。Attentive FP的特征可视化表明，它可以从指定的任务中自动学习非局部分子内相互作用，这可以帮助我们直接从人类感知之外的数据中获得化学见解。
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for the rising amounts of data, but the gap between what these neural networks learn and what human beings can comprehend is growing. Moreover, this gap may induce distrust and restrict deep learning applications in practice. Here, we introduce a new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery data sets. We demonstrate that Attentive FP achieves state-of-the-art predictive performances on a variety of data sets and that what it learns is interpretable. The feature visualization for Attentive FP suggests that it automatically learns nonlocal intramolecular interactions from specified tasks, which can help us gain chemical insights directly from data beyond human perception.