Novel drug-target interactions via link prediction and network embedding.
Novel drug-target interactions via link prediction and network embedding.
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DOI：
10.1186/s12859-022-04650-w
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发表时间：
2022-04-04
影响因子：
3
通讯作者：
Tsoka S
中科院分区：
文献类型：
--
作者：
Amiri Souri E;Laddach R;Karagiannis SN;Papageorgiou LG;Tsoka S
由于化学和基因组空间之间的许多相互作用尚未被发现，能够识别潜在药物-靶标相互作用（DTIs）的计算方法被用来加速药物发现并降低所需的成本。预测新的dti可以通过确定已批准药物的新靶点来利用药物再利用。然而，开发一个精确的计算框架，可以有效地结合化学和基因组空间仍然是非常苛刻的。一个关键问题是，大多数DTI预测缺乏实验验证的负相互作用或目标3D结构的有限可用性。我们报告了DT2Vec，一个基于图嵌入和梯度增强树分类的DTI预测管道。它将药物-药物和蛋白质-蛋白质相似网络映射到低维特征，DTI预测是基于连接药物和目标嵌入向量作为输入特征的策略而制定的二分类。在一个标准基准数据集上，将DT2Vec与三种性能最好的基于图相似度的算法进行了比较，取得了具有竞争力的结果。为了探索可信的新型dti，该模型应用于ChEMBL存储库中的数据，这些数据包含经过实验验证的积极和消极相互作用，从而产生强大的预测模型。然后，将所建立的模型应用于所有可能的未知dti来预测新的相互作用。通过案例研究讨论了DT2Vec作为药物再利用有效方法的适用性，并利用分子对接对一些新的DTI预测进行了评估。该方法能够将化学空间和基因组空间整合并映射到低维密集向量中，并在预测新型dti方面显示出良好的结果。在线版本包含补充材料，可在10.1186/s12859-022-04650-w获得。
As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by identifying new targets for approved drugs. However, developing an accurate computational framework that can efficiently incorporate chemical and genomic spaces remains extremely demanding. A key issue is that most DTI predictions suffer from the lack of experimentally validated negative interactions or limited availability of target 3D structures. We report DT2Vec, a pipeline for DTI prediction based on graph embedding and gradient boosted tree classification. It maps drug-drug and protein–protein similarity networks to low-dimensional features and the DTI prediction is formulated as binary classification based on a strategy of concatenating the drug and target embedding vectors as input features. DT2Vec was compared with three top-performing graph similarity-based algorithms on a standard benchmark dataset and achieved competitive results. In order to explore credible novel DTIs, the model was applied to data from the ChEMBL repository that contain experimentally validated positive and negative interactions which yield a strong predictive model. Then, the developed model was applied to all possible unknown DTIs to predict new interactions. The applicability of DT2Vec as an effective method for drug repurposing is discussed through case studies and evaluation of some novel DTI predictions is undertaken using molecular docking. The proposed method was able to integrate and map chemical and genomic space into low-dimensional dense vectors and showed promising results in predicting novel DTIs. The online version contains supplementary material available at 10.1186/s12859-022-04650-w.
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DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
影响因子：
9.5
作者：
Hao, Ming;Bryant, Stephen H.;Wang, Yanli
通讯作者：
Wang, Yanli
DOI：
10.1145/2939672.2939754
发表时间：
2016-08
期刊：
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Grover A;Leskovec J
通讯作者：
Leskovec J
影响因子：
3.5
作者：
Cardoso-Silva, Jonathan;Papageorgiou, Lazaros G.;Tsoka, Sophia
通讯作者：
Tsoka, Sophia
影响因子：
2.7
作者：
Condon, Jeffrey S.;Joseph-McCarthy, Diane;Zhang, Yuhua
通讯作者：
Zhang, Yuhua