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DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
Journal of Cheminformatics ( IF 8.6 ) Pub Date : 2020-06-29 , DOI: 10.1186/s13321-020-00447-2
Maha A. Thafar , Rawan S. Olayan , Haitham Ashoor , Somayah Albaradei , Vladimir B. Bajic , Xin Gao , Takashi Gojobori , Magbubah Essack

In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

中文翻译:

DTiGEMS +:使用图嵌入,图挖掘和基于相似性的技术进行药物-靶标相互作用预测

在计算机模拟中,药物与目标相互作用的预测是可持续药物开发过程中的关键阶段,尤其是当研究重点是利用现有药物的重新定位时。但是,开发这样的计算方法不是一件容易的事,而是非常需要的,因为当前预测潜在的药物-靶标相互作用的方法存在很高的假阳性率。在这里,我们介绍DTiGEMS +,这是一种使用图嵌入,图挖掘和基于相似性的技术预测药物-靶标相互作用的计算方法。DTiGEMS +结合了基于相似度和基于特征的方法,并在异质网络中将识别新的药物与靶标相互作用建模为链接预测问题。DTiGEMS +通过将已知的药物-靶标相互作用图与另外两个互补图进行扩充来构建异质网络:药物-药物相似性,靶标-靶标相似性。DTiGEMS +结合了不同的计算技术来提供最终的药物靶标预测,这些技术包括图形嵌入,图形挖掘和机器学习。在应用相似性选择程序和相似性融合算法后,DTiGEMS +将多个药物-药物相似性和靶标-靶标相似性整合到最终的异构图构造中。使用四个基准数据集,我们发现DTiGEMS +与通过开发所有数据集的最高平均AUPR(0.92)预测药物-靶标相互作用而开发的其他最新计算机模拟方法相比,大大提高了预测性能,
更新日期:2020-06-29
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