Shi Jian-Yu, Yiu Siu-Ming, Li Yiming, Leung Henry C M, Chin Francis Y L
School of Life Sciences, Northwestern Polytechnical University, No. 127, Youyi Road West, Xi'an, Shaanxi 710072, China.
Department of Computer Science, The University of Hong Kong, Pokfulam Road, Hong Kong.
Methods. 2015 Jul 15;83:98-104. doi: 10.1016/j.ymeth.2015.04.036. Epub 2015 May 6.
Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html.
使用计算方法预测药物-靶点相互作用是药物发现和重新定位中的重要一步。为了预测一种药物和一个靶点之间是否会发生相互作用，大多数现有方法在数据库中识别相似的药物和靶点。然后基于这些药物和靶点的已知相互作用进行预测。这个想法很有前景。然而，有两个缺点尚未得到妥善解决。首先，大多数方法仅分别使用二维化学结构和蛋白质序列来衡量药物和靶点的相似性。然而，这些信息可能无法完全捕捉决定药物是否会与靶点相互作用的特征。其次，已知的相互作用非常少，即数据库中许多相互作用是“缺失”的。现有方法偏向于已知的相互作用，并且没有很好的解决方案来处理可能缺失的相互作用，这影响了预测的准确性。在本文中，我们增强了相似性度量以纳入非结构（和非基于序列的）信息，并引入“超级靶点”的概念来处理可能缺失的相互作用问题。基于对真实数据的评估，我们表明我们的相似性度量优于现有度量，并且我们的方法能够比现有的两种最佳算法WNN-GIP和KBMF2K实现更高的准确性。我们的方法可在http://web.hku.hk/∼liym1018/projects/drug/drug.html或http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html获取。
