College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Biomed Res Int. 2013;2013:485034. doi: 10.1155/2013/485034. Epub 2013 Sep 4.
A drug side effect is an undesirable effect which occurs in addition to the intended therapeutic effect of the drug. The unexpected side effects that many patients suffer from are the major causes of large-scale drug withdrawal. To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs. In this study, a novel computational method was developed to predict the side effects of drug compounds by hybridizing the chemical-chemical and protein-chemical interactions. Compared to most of the previous works, our method can rank the potential side effects for any query drug according to their predicted level of risk. A training dataset and test datasets were constructed from the benchmark dataset that contains 835 drug compounds to evaluate the method. By a jackknife test on the training dataset, the 1st order prediction accuracy was 86.30%, while it was 89.16% on the test dataset. It is expected that the new method may become a useful tool for drug design, and that the findings obtained by hybridizing various interactions in a network system may provide useful insights for conducting in-depth pharmacological research as well, particularly at the level of systems biomedicine.
药物副作用是指除药物的预期治疗效果之外出现的不良影响。许多患者遭受的意外副作用是大规模药物撤市的主要原因。为了解决这个问题，制药行业非常需要开发用于预测药物副作用的计算方法。在这项研究中，通过混合化学-化学和蛋白质-化学相互作用，开发了一种新的计算方法来预测药物化合物的副作用。与大多数先前的工作相比，我们的方法可以根据预测的风险水平对任何查询药物的潜在副作用进行排序。使用包含 835 种药物化合物的基准数据集构建了训练数据集和测试数据集，以评估该方法。通过对训练数据集进行自举测试，一阶预测准确率为 86.30%，而在测试数据集上则为 89.16%。预计新方法可能成为药物设计的有用工具，并且在网络系统中混合各种相互作用获得的发现也可能为深入的药理学研究提供有用的见解，特别是在系统生物医学水平上。
