Analysis and prediction of drug–drug interaction by minimum redundancy maximum relevance and incremental feature selection

Drug–drug interaction (DDI) defines a situation in which one drug affects the activity of another when both are administered together. DDI is a common cause of adverse drug reactions and sometimes also leads to improved therapeutic effects. Therefore, it is of great interest to discover novel DDIs according to their molecular properties and mechanisms in a robust and rigorous way. This paper attempts to predict effective DDIs using the following properties: (1) chemical interaction between drugs; (2) protein interactions between the targets of drugs; and (3) target enrichment of KEGG pathways. The data consisted of 7323 pairs of DDIs collected from the DrugBank and 36,615 pairs of drugs constructed by randomly combining two drugs. Each drug pair was represented by 465 features derived from the aforementioned three categories of properties. The random forest algorithm was adopted to train the prediction model. Some feature selection techniques, including minimum redundancy maximum relevance and incremental feature selection, were used to extract key features as the optimal input for the prediction model. The extracted key features may help to gain insights into the mechanisms of DDIs and provide some guidelines for the relevant clinical medication developments, and the prediction model can give new clues for identification of novel DDIs.

药物相互作用（Drug-drug interaction，DDI）指两种药物联合使用时，其中一种药物会影响另一种药物活性的情况。DDI是药物不良反应的常见诱因，有时也可带来治疗效果的提升。因此，以稳健且严谨的方式，基于药物的分子属性与作用机制发现新型DDI具有重要研究价值。本文尝试基于以下三类属性预测有效DDI：（1）药物间的化学相互作用；（2）药物靶点之间的蛋白质相互作用；（3）KEGG通路的靶点富集特征。本数据集包含从DrugBank数据库中采集的7323对已知DDI样本，以及通过随机组合两种药物构建的36615对药物对样本。每一对药物样本均通过465个特征进行表征，这些特征源自上述三类属性。本研究采用随机森林算法训练预测模型，并使用包括最小冗余最大相关（minimum redundancy maximum relevance）与增量特征选择在内的多种特征选择技术，提取关键特征作为预测模型的最优输入。所提取的关键特征有助于深入解析DDI的作用机制，为相关临床用药研发提供参考依据，而该预测模型也可为新型DDI的识别提供新的线索。