Fan Jun, Yang Jing, Jiang Zhenran
1 Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University , Shanghai, China .
2 Department of Computer Science and Technology, East China Normal University , Shanghai, China .
J Comput Biol. 2018 Apr;25(4):435-443. doi: 10.1089/cmb.2017.0149. Epub 2017 Oct 23.
Drug side effects are one of the public health concerns. Using powerful machine-learning methods to predict potential side effects before the drugs reach the clinical stages is of great importance to reduce time consumption and protect the security of patients. Recently, researchers have proved that the central nervous system (CNS) side effects of a drug are closely related to its permeability to the blood-brain barrier (BBB). Inspired by this, we proposed an extended neighborhood-based recommendation method to predict CNS side effects using drug permeability to the BBB and other known features of drug. To the best of our knowledge, this is the first attempt to predict CNS side effects considering drug permeability to the BBB. Computational experiments demonstrated that drug permeability to the BBB is an important factor in CNS side effects prediction. Moreover, we built an ensemble recommendation model and obtained higher AUC score (area under the receiver operating characteristic curve) and AUPR score (area under the precision-recall curve) on the data set of CNS side effects by integrating various features of drug.
药物副作用是公共卫生关注的问题之一。在药物进入临床阶段之前，使用强大的机器学习方法预测潜在副作用对于减少时间消耗和保护患者安全至关重要。最近，研究人员证明药物的中枢神经系统（CNS）副作用与其血脑屏障（BBB）通透性密切相关。受此启发，我们提出了一种基于扩展邻域的推荐方法，利用药物对BBB的通透性和药物的其他已知特征来预测CNS副作用。据我们所知，这是首次尝试考虑药物对BBB的通透性来预测CNS副作用。计算实验表明，药物对BBB的通透性是CNS副作用预测中的一个重要因素。此外，我们构建了一个集成推荐模型，并通过整合药物的各种特征，在CNS副作用数据集上获得了更高的AUC分数（受试者工作特征曲线下面积）和AUPR分数（精确召回率曲线下面积）。
