Graph neural network approaches for drug-target interactions
Graph neural network approaches for drug-target interactions
复制标题
用于药物-靶标相互作用的图神经网络方法
DOI：
10.1016/j.sbi.2021.102327
复制
发表时间：
2022-01-21
影响因子：
6.8
通讯作者：
Li, Xutong
中科院分区：
文献类型：
--
作者：
Zhang, Zehong;Chen, Lifan;Li, Xutong
开发新药仍然非常昂贵、耗时，而且常常涉及安全问题。准确预测药物-靶点相互作用（DTI）可以指导药物发现过程，从而促进药物开发。非欧几里得数据，例如类药物分子结构、关键口袋残基结构和蛋白质相互作用网络，可以使用图表有效地表示。因此，新兴的图神经网络已被迅速应用于预测DTI，并被证明在寻找重新定位药物和加速药物发现方面有效。在这篇综述中，我们简要概述了 DTI 模型中使用的深度神经网络。然后，我们总结了DTI预测所需的数据库，并全面介绍了图神经网络在DTI预测中的应用。我们还强调了当前的挑战和未来的方向，以指导该领域的进一步发展。
Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development. Non -Euclidian data such as drug-like molecule structures, key pocket residue structures, and protein interaction networks can be represented effectively using graphs. Therefore, the emerging graph neural network has been rapidly applied to predict DTIs, and proved effective in finding repositioning drugs and accelerating drug discovery. In this review, we provide a brief overview of deep neural networks used in DTI models. Then, we summarize the database required for DTI prediction, followed by a comprehensive introduction of applications of graph neural networks for DTI prediction. We also highlight current challenges and future directions to guide the further development of this field.