Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile.
Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile.
Int J Mol Sci. 2022 Aug 26;23(17):9666. doi: 10.3390/ijms23179666.
Identifying drug-target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug-drug-target network constructed from protein-ligand interaction annotations and drug-drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.
鉴定药物-靶点相互作用是发现新药物和药物重定位的关键步骤。由于能够直接整合来自不同来源的信息，并且有可能从图拓扑结构中提取新信息，基于网络的方法显示出了巨大的潜力。然而，尽管最近取得了一些进展，但仍然迫切需要高效和稳健的预测方法。在这里，我们提出了 SimSpread，这是一种将基于网络的推理与化学相似性相结合的新方法。该方法使用从蛋白质-配体相互作用注释和药物-药物化学相似性构建的三部分药物-药物-靶点网络，资源传播算法预测已知或失败药物和新型化合物的潜在生物靶点。我们将小分子表示为与其他化合物的相似性指数向量，从而提供了一种灵活的方法来探索不同的分子表示。我们通过在一系列数据集上进行多次交叉验证和时间分割验证过程，证明了我们提出的方法具有较高的预测性能。此外，我们还证明了我们的方法在化学配体空间（支架跳跃）和生物靶标空间（靶标跳跃）之间进行了平衡的探索。我们的结果表明该方法具有稳健和平衡的性能，并且可能有助于预测药物靶点、虚拟筛选和药物重定位。
