GraphDTA: predicting drug-target binding affinity with graph neural networks
GraphDTA: predicting drug-target binding affinity with graph neural networks
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DOI：
10.1093/bioinformatics/btaa921
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发表时间：
2021-04-15
期刊：
影响因子：
5.8
通讯作者：
Venkatesh, Svetha
中科院分区：
文献类型：
--
作者：
Thin Nguyen;Hang Le;Venkatesh, Svetha
A Summary: The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational models that estimate the interaction strength of new drug-target pairs have the potential to expedite drug repurposing. Several models have been proposed for this task. However, these models represent the drugs as strings, which is not a natural way to represent molecules. We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug-target affinity. We show that graph neural networks not only predict drug-target affinity better than non-deep learning models, but also outperform competing deep learning methods. Our results confirm that deep learning models are appropriate for drugtarget binding affinity prediction, and that representing drugs as graphs can lead to further improvements.
