School of Informatics, Xiamen University, Xiamen, China.
Shuye Technology Co., Ltd., Hangzhou, China.
BMC Bioinformatics. 2021 Nov 26;22(1):567. doi: 10.1186/s12859-021-04476-y.
Experimental verification of a drug discovery process is expensive and time-consuming. Therefore, recently, the demand to more efficiently and effectively identify drug-target interactions (DTIs) has intensified.
We treat the prediction of DTIs as a ranking problem and propose a neural network architecture, NeuRank, to address it. Also, we assume that similar drug compounds are likely to interact with similar target proteins. Thus, in our model, we add drug and target similarities, which are very effective at improving the prediction of DTIs. Then, we develop NeuRank from a point-wise to a pair-wise, and further to list-wise model.
Finally, results from extensive experiments on five public data sets (DrugBank, Enzymes, Ion Channels, G-Protein-Coupled Receptors, and Nuclear Receptors) show that, in identifying DTIs, our models achieve better performance than other state-of-the-art methods.
药物发现过程的实验验证既昂贵又耗时。因此，最近人们对更高效、更有效地识别药物-靶标相互作用（DTIs）的需求日益强烈。
我们将 DTIs 的预测视为排序问题，并提出了一种神经网络架构 NeuRank 来解决它。此外，我们假设类似的药物化合物可能与类似的靶蛋白相互作用。因此，在我们的模型中，我们添加了药物和靶标相似度，这对提高 DTIs 的预测非常有效。然后，我们从点到对，再到列表的模型开发 NeuRank。
最后，在五个公共数据集（DrugBank、Enzymes、Ion Channels、G-Protein-Coupled Receptors 和 Nuclear Receptors）上进行的广泛实验结果表明，在识别 DTIs 方面，我们的模型比其他最先进的方法表现更好。
