Shen Ying, Yuan Kaiqi, Yang Min, Tang Buzhou, Li Yaliang, Du Nan, Lei Kai
The Shenzhen Key Lab for Information Centric Networking and Blockchain Techologies(ICNLab), School of Electronics and Computer Engineering, Peking University Shenzhen Graduate School, 518055, Shenzhen, People's Republic of China.
SIAT, Chinese Academy of Sciences, 518055, Shenzhen, People's Republic of China.
J Cheminform. 2019 Mar 14;11(1):22. doi: 10.1186/s13321-019-0342-y.
Efficient representations of drugs provide important support for healthcare analytics, such as drug-drug interaction (DDI) prediction and drug-drug similarity (DDS) computation. However, incomplete annotated data and drug feature sparseness create substantial barriers for drug representation learning, making it difficult to accurately identify new drug properties prior to public release. To alleviate these deficiencies, we propose KMR, a knowledge-oriented feature-driven method which can learn drug related knowledge with an accurate representation. We conduct series of experiments on real-world medical datasets to demonstrate that KMR is capable of drug representation learning. KMR can support to discover meaningful DDI with an accuracy rate of 92.19%, demonstrating that techniques developed in KMR significantly improve the prediction quality for new drugs not seen at training. Experimental results also indicate that KMR can identify DDS with an accuracy rate of 88.7% by facilitating drug knowledge, outperforming existing state-of-the-art drug similarity measures.
药物的有效表示为医疗保健分析提供了重要支持，例如药物相互作用（DDI）预测和药物相似性（DDS）计算。然而，注释数据不完整和药物特征稀疏给药物表示学习带来了巨大障碍，使得在新药公开发布之前难以准确识别其新特性。为了缓解这些不足，我们提出了KMR，一种面向知识的特征驱动方法，它可以通过准确的表示来学习与药物相关的知识。我们在真实世界的医学数据集上进行了一系列实验，以证明KMR能够进行药物表示学习。KMR能够以92.19%的准确率支持发现有意义的药物相互作用，这表明KMR中开发的技术显著提高了对训练中未出现的新药的预测质量。实验结果还表明，KMR通过促进药物知识能够以88.7%的准确率识别药物相似性，优于现有的最先进的药物相似性度量。
