Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. In this study, a novel framework SNF-NN based on deep learning is presented, where novel drug-disease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods ( $$AUC-ROC$$ = 0.867, and $$AUC-PR$$ =0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework’s performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with $$AUC-ROC$$ ranging from 0.879 to 0.931 and $$AUC-PR$$ from 0.856 to 0.903. Moreover, the efficiency of SNF-NN is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions. The data and implementation of SNF-NN are available at http://pages.cpsc.ucalgary.ca/ tnjarada/snf-nn.php .

中文翻译：

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SNF-NN：使用相似网络融合和神经网络预测药物与疾病相互作用的计算方法

药物重新定位是药物研究中的一种新兴方法，用于识别已批准药物的新治疗潜力并发现未治疗疾病的治疗方法。与传统的从头药物发现过程相比，由于其时间和成本效率，药物重新定位在优化药物开发过程中发挥着重要作用。基因组学的进步，加上大规模公开数据的巨大增长和高性能计算能力的可用性，进一步推动了计算药物重新定位方法的发展。最近，机器学习技术的兴起，加上功能强大的计算机的可用性，使得计算药物重新定位领域成为一个活跃的领域。在这项研究中，提出了一种基于深度学习的新框架SNF-NN，其中使用药物相关相似性信息、疾病相关相似性信息和已知药物-疾病相互作用来预测新的药物-疾病相互作用。与药物和疾病相关的异质相似性信息被输入到所提出的框架中，以预测新的药物与疾病的相互作用。SNF-NN 使用相似性选择、相似性网络融合和高度调整的新型神经网络模型来预测新的药物与疾病的相互作用。通过将 SNF-NN 的性能与九种基线机器学习方法进行比较来评估其鲁棒性。使用分层 10 倍交叉验证，所提出的框架优于所有基线方法（$$AUC-ROC$$ = 0.867 和 $$AUC-PR$$ =0.876）。为了进一步证明 SNF-NN 的可靠性和鲁棒性，使用两个数据集来相对于七种最新的药物与疾病相互作用预测方法来公平地验证所提出的框架的性能。SNF-NN 在分层 10 倍交叉验证中取得了显着的性能，$$AUC-ROC$$ 范围为 0.879 至 0.931，$$AUC-PR$$ 范围为 0.856 至 0.903。此外，通过根据临床试验和已发表的研究验证预测的未知药物与疾病的相互作用，验证了 SNF-NN 的效率。总之，计算药物重新定位研究可以从异构网络和深度学习模型中集成相似性度量以预测新的药物与疾病相互作用中获益匪浅。SNF-NN 的数据和实现可在 http://pages.cpsc.ucalgary.ca/tnjarada/snf-nn.php 获取。