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. 2022 Mar 4;14:9. doi: 10.1186/s13321-022-00589-5

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Overview of the drug-drug interaction (DDI) prediction model. A Datasets are preprocessed for each feature generation model and DDI prediction model. B Given drug-pair and side-effect data, each drug feature is obtained using a feature generation model and a side effect is embedded to latent space; then, drugs pass through neural networks having gated linear units to consider the pair information. Finally, using latent representations of drugs and the side effect, the side-effect score is calculated. C The model is evaluated considering three DDI cases. D, E The independent dataset from DrugBank is used for external validation