Institute of Systems Biology, Shanghai University, Shanghai 200444, China ; College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Biomed Res Int. 2013;2013:723780. doi: 10.1155/2013/723780. Epub 2013 Sep 5.
Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations.
药物联合治疗在治疗某些复杂疾病方面可能比单一药物更有效，因为它具有更好的疗效和更低的副作用。尽管一些药物联合治疗已经在使用，但它们的潜在分子机制仍知之甚少。因此，以稳健和严格的方式从分子机制上推导出新的药物组合是非常有趣的。本文试图通过综合考虑以下三个方面来预测有效的药物组合：（1）药物之间的化学相互作用，（2）药物靶点之间的蛋白质相互作用，以及（3）KEGG 途径的靶标富集。构建了一个基准数据集，其中包含 121 种已确认的有效组合和 605 种随机组合。每个药物组合由来自上述三个特性的 465 个特征表示。采用了一些特征选择技术，包括最小冗余最大相关性和增量特征选择，以提取关键特征。随机森林模型建立，并通过 5 折交叉验证评估其性能。结果，选择了 55 个提供最佳预测结果的关键特征。这些重要特征可能有助于深入了解药物组合的机制，并且所提出的预测模型可能成为筛选可能的药物组合的有用工具。
