Laenen Griet, Thorrez Lieven, Börnigen Daniela, Moreau Yves
KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Leuven, Belgium.
Mol Biosyst. 2013 Jul;9(7):1676-85. doi: 10.1039/c3mb25438k. Epub 2013 Feb 26.
Polypharmacology, which focuses on designing drugs that bind efficiently to multiple targets, has emerged as a new strategic trend in today's drug discovery research. Many successful drugs achieve their effects via multi-target interactions. However, these targets are largely unknown for both marketed drugs and drugs in development. A better knowledge of a drug's mode of action could be of substantial value to future drug development, in particular for side effect prediction and drug repositioning. We propose a network-based computational method for drug target prediction, applicable on a genome-wide scale. Our approach relies on the analysis of gene expression following drug treatment in the context of a functional protein association network. By diffusing differential expression signals to neighboring or correlated nodes in the network, genes are prioritized as potential targets based on the transcriptional response of functionally related genes. Different diffusion strategies were evaluated on 235 publicly available gene expression datasets for treatment with bioactive molecules having a known target. AUC values of up to more than 90% demonstrate the effectiveness of our approach and indicate the predictive power of integrating experimental gene expression data with prior knowledge from protein association networks.
多药理学专注于设计能有效结合多个靶点的药物，已成为当今药物研发研究中的一种新战略趋势。许多成功的药物通过多靶点相互作用发挥作用。然而，对于已上市药物和处于研发阶段的药物，这些靶点大多尚不清楚。更好地了解药物的作用模式对未来药物研发可能具有重大价值，特别是在副作用预测和药物重新定位方面。我们提出了一种基于网络的药物靶点预测计算方法，适用于全基因组范围。我们的方法依赖于在功能蛋白关联网络的背景下分析药物处理后的基因表达。通过将差异表达信号扩散到网络中的相邻或相关节点，根据功能相关基因的转录反应将基因优先列为潜在靶点。在235个公开可用的基因表达数据集上，针对已知靶点的生物活性分子处理情况评估了不同的扩散策略。高达90%以上的AUC值证明了我们方法的有效性，并表明将实验基因表达数据与蛋白质关联网络的先验知识相结合的预测能力。
