IEEE/ACM Trans Comput Biol Bioinform. 2021 Jul-Aug;18(4):1562-1571. doi: 10.1109/TCBB.2019.2951557. Epub 2021 Aug 6.
Combinatorial drug therapy is a promising way for treating cancers, which can reduce drug side effects and improve drug efficacy. However, due to the large-scale combinatorial space, it is difficult to quickly and effectively identify novel synergistic drug combinations for further implementing combinatorial drug therapy. The computational method of fusing multi-source knowledge is a time- and cost-efficient strategy to infer synergistic drug combinations for testing. However, for the existing computational methods of inferring synergistic drug combinations, it still remains a challenging to effectively combine multi-source information to achieve the desired results. Hence, in this study, we developed a novel Inference method of Synergistic Drug Combinations based on Symmetric Meta-Path (ISDCSMP), which can systematically and accurately prioritize synergistic drug combinations in a novel drug-target heterogeneous network integrating multi-source information. In the experiment, ISDCSMP outperformed the state-of-the-art methods in terms of AUC and precision on the benchmark dataset in five-fold cross validation. Moreover, we further illustrated performances of different ways for obtaining the combination coefficients, and analyzed the influences of the maximum meta-path length. The performances of various single meta-paths were described in five-fold cross validation. Finally, we confirmed the practical usefulness of ISDCSMP with the predicted novel synergistic drug combinations. The source code of ISDCSMP is available at https://github.com/KDDing/ISDCSMP.
联合药物治疗是治疗癌症的一种很有前途的方法，它可以降低药物的副作用，提高药物的疗效。然而，由于组合空间规模庞大，因此很难快速有效地识别出新颖的协同药物组合，以便进一步实施联合药物治疗。融合多源知识的计算方法是推断协同药物组合以进行测试的一种省时省钱的策略。然而，对于现有的推断协同药物组合的计算方法，仍然存在一个挑战，即如何有效地结合多源信息来实现预期的结果。因此，在本研究中，我们开发了一种新的基于对称元路径的协同药物组合推断方法（ISDCSMP），该方法可以系统而准确地对整合多源信息的新型药物-靶标异质网络中的协同药物组合进行优先级排序。在实验中，ISDCSMP 在五重交叉验证的基准数据集上的 AUC 和精度方面均优于最先进的方法。此外，我们进一步说明了获取组合系数的不同方法的性能，并分析了最大元路径长度的影响。在五重交叉验证中描述了各种单条元路径的性能。最后，我们通过预测的新型协同药物组合证实了 ISDCSMP 的实际用途。ISDCSMP 的源代码可在 https://github.com/KDDing/ISDCSMP 上获得。
