Autoencoder-based drug-target interaction prediction by preserving the consistency of chemical properties and functions of drugs
Autoencoder-based drug-target interaction prediction by preserving the consistency of chemical properties and functions of drugs
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DOI：
10.1093/bioinformatics/btab384
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发表时间：
2021-05-21
期刊：
影响因子：
5.8
通讯作者：
Liu, Jian
中科院分区：
文献类型：
--
作者：
Sun, Chang;Cao, Yangkun;Liu, Jian
动机：探索潜在的药物-靶标相互作用（DT）是药物发现和再利用的关键步骤。近年来，通过计算方法预测可能的DTI逐渐成为一个研究热点。然而，大多数以前的研究未能明智地考虑到药物的化学性质与其功能之间的一致性。这些关系的变化可能会导致一个严重的负面影响预测的DTIs.Results：我们提出了一个自动编码器为基础的方法，AEFS，在空间一致性约束下预测DTIs。建立了一个异构网络，整合药物、蛋白质和疾病的信息。原始药物特征通过多层编码器投影到嵌入（蛋白质）空间，并进一步通过解码器投影到标签（疾病）空间。在这个过程中，药物的临床信息被引入，以辅助DTI预测。AEFS通过保持药物相关性在原始特征空间、嵌入空间和标签空间的分布，保持了药物化学性质和功能的一致性。实验比较表明，AEFS对不平衡数据具有更强的鲁棒性，在DTI预测中具有明显的上级性能。案例研究进一步证实了其挖掘潜在DTI的能力。
Motivation: Exploring the potential drug-target interactions (DT's) is a key step in drug discovery and repurposing. In recent years, predicting the probable DTIs through computational methods has gradually become a research hot spot. However, most of the previous studies failed to judiciously take into account the consistency between the chemical properties of drug and its functions. The changes of these relationships may lead to a severely negative effect on the prediction of DTIs.Results: We propose an autoencoder-based method, AEFS, under spatial consistency constraints to predict DTIs. A heterogeneous network is established to integrate the information of drugs, proteins and diseases. The original drug features are projected to an embedding (protein) space by a multi-layer encoder, and further projected into label (disease) space by a decoder. In this process, the clinical information of drugs is introduced to assist the DTI prediction. By maintaining the distribution of drug correlation in the original feature, embedding and label space, AEFS keeps the consistency between chemical properties and functions of drugs. Experimental comparisons indicate that AEFS is more robust for imbalanced data and of significantly superior performance in DTI prediction. Case studies further confirm its ability to mine the latent DTIs.