Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.
Drugs provide help and promise for human health, but they usually come with side effects. Predicting side effects of drugs is a critical issue for the drug discovery. Although several machine-learning methods were proposed to predict the drug side effects, it remains the space for the improvement. To the best of our knowledge, many side effects are not detectable in clinical trials until drugs are approved, thus predicting potential or missing side effects based on the known side effects is important for the postmarketing surveillance. In order to solve this specific problem, we formulate approved drugs, side effect terms and drug-side effect associations as a recommender system, and transform the problem of predicting side effects into a recommender task. Two recommender methods, i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method, are designed to make predictions. Further, in order to achieve better performances, we combine proposed methods and existing methods of the same type to develop ensemble models. Compared with benchmark methods, the proposed methods and the ensemble method lead to better performances, and the statistical analysis demonstrates the improvements are significant (p-value<0.05). In conclusion, the integrated neighborhood-based method, the restricted Boltzmann machine-based method and the ensemble method are promising tools for the side effect prediction. The source codes and datasets are provided as the supplementary. Predicting side effects of drugs is a critical issue for the drug discovery.We transform approved drugs, side effect terms and drug-side effect associations as a recommender system.We design two recommender methods, i.e. the integrated neighborhood-based method and the restricted Boltzmann machine-based method, to make predictions.Further, we combine proposed methods and existing methods of the same type to develop ensemble models.  Drug-drug interactions (DDIs) are one of the major concerns in drug discovery. Accurate prediction of potential DDIs can help to reduce unexpected interactions in the entire lifecycle of drugs, and are important for the drug safety surveillance. Since many DDIs are not detected or observed in clinical trials, this work is aimed to predict unobserved or undetected DDIs. In this paper, we collect a variety of drug data that may influence drug-drug interactions, i.e., drug substructure data, drug target data, drug enzyme data, drug transporter data, drug pathway data, drug indication data, drug side effect data, drug off side effect data and known drug-drug interactions. We adopt three representative methods: the neighbor recommender method, the random walk method and the matrix perturbation method to build prediction models based on different data. Thus, we evaluate the usefulness of different information sources for the DDI prediction. Further, we present flexible frames of integrating different models with suitable ensemble rules, including weighted average ensemble rule and classifier ensemble rule, and develop ensemble models to achieve better performances. The experiments demonstrate that different data sources provide diverse information, and the DDI network based on known DDIs is one of most important information for DDI prediction. The ensemble methods can produce better performances than individual methods, and outperform existing state-of-the-art methods. The datasets and source codes are available at https: github.com zw9977129 drug-drug-interaction .