iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding
iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding
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DOI：
10.1371/journal.pcbi.1008040
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发表时间：
2020-07-01
影响因子：
4.3
通讯作者：
Li, Jing
中科院分区：
文献类型：
--
作者：
Chen, Huiyuan;Cheng, Feixiong;Li, Jing
计算药物重新定位和药物靶点预测已成为药物发现早期阶段的重要任务。在以前的研究中，这两项任务往往被分开考虑。然而，在这两项任务中研究的实体（即，药物、靶点和疾病）是内在相关的。一方面，药物与细胞中的靶点相互作用以调节靶点活性，从而改变生物学途径以促进健康功能和治疗疾病。另一方面，药物重新定位和药物靶点预测都涉及相同的药物特征空间，这自然将这两个问题和两个领域（疾病和靶点）联系起来。通过使用群众的智慧，可以将知识从一个领域转移到另一个领域。药物-靶点-疾病之间的关系促使我们在药物发现中将药物重新定位和药物-靶点预测结合起来考虑。在本文中，我们提出了一种称为iDrug的新方法，它通过跨网络嵌入将药物重新定位和药物靶点预测无缝集成到一个连贯的模型中。特别是，我们提供了一个原则性的方法来转移这两个领域的知识，并提高这两个任务的预测性能。使用真实世界的数据集，我们证明了iDrug在两个学习任务上都取得了上级性能，与几种最先进的方法相比。
Computational drug repositioning and drug-target prediction have become essential tasks in the early stage of drug discovery. In previous studies, these two tasks have often been considered separately. However, the entities studied in these two tasks (i.e., drugs, targets, and diseases) are inherently related. On one hand, drugs interact with targets in cells to modulate target activities, which in turn alter biological pathways to promote healthy functions and to treat diseases. On the other hand, both drug repositioning and drug-target prediction involve the same drug feature space, which naturally connects these two problems and the two domains (diseases and targets). By using the wisdom of the crowds, it is possible to transfer knowledge from one of the domains to the other. The existence of relationships among drug-target-disease motivates us to jointly consider drug repositioning and drug-target prediction in drug discovery. In this paper, we present a novel approach called iDrug, which seamlessly integrates drug repositioning and drug-target prediction into one coherent model via cross-network embedding. In particular, we provide a principled way to transfer knowledge from these two domains and to enhance prediction performance for both tasks. Using real-world datasets, we demonstrate that iDrug achieves superior performance on both learning tasks compared to several state-of-the-art approaches.