Zhang Yuanyuan, Wu Mengjie, Wang Shudong, Chen Wei
School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China.
College of Computer science and Technology, China University of Petroleum (East China), Qingdao, Shandong, China.
Front Pharmacol. 2022 Sep 23;13:1009996. doi: 10.3389/fphar.2022.1009996. eCollection 2022.
Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.
准确识别药物-靶点相互作用（DTIs）对于理解药物治疗机制和发现治疗疾病的新药具有重要意义。目前，结合药物和靶点多源数据的DTIs预测计算方法能够有效降低药物研发的成本和时间。然而，在多源数据处理中，不同源数据对DTIs的贡献往往未被考虑。因此，如何充分利用不同源数据的贡献来预测DTIs以实现高效融合是提高DTIs预测准确性的关键。本文考虑不同源数据对DTIs预测的贡献，提出了一种基于药物和靶点多源数据有效融合的DTIs预测方法，称为EFMSDTI。EFMSDTI首先根据药物和靶点的生物学特征，基于分类为拓扑图和语义图的多源信息网络构建15个相似性网络。然后，根据多网络对DTIs预测的贡献，通过基于相似性网络融合（SNF）的选择性和熵加权对多网络进行融合。深度神经网络模型学习药物和靶点的低维向量嵌入。最后，使用基于梯度提升决策树（GBDT）的LightGBM算法完成DTIs预测。实验结果表明，EFMSDTI比几种现有算法具有更好的性能（AUROC和AUPR分别为0.982）。此外，它在分析前1000个预测结果方面有良好效果，前1000个DTIs中有990个得到了证实。代码和数据可在https://github.com/meng-jie/EFMSDTI获取。
