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. 2019 Sep 20;9:13645. doi: 10.1038/s41598-019-50121-3

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© The Author(s) 2019

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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The scheme of NDD workflow. (a) Selecting the best subset of similarity matrices. (b) Applying SNF, a fusion method, to integrate all selected similarities into an m*m matrix where m is the number of drugs. (c) Every row in the integrated matrix, is the feature vector of its corresponding drug. (d) For each pair of the drugs, their feature vectors are concatenated in a vector and is considered as the input of a neural network. (e) The neural network is applied to the input vector to calculate the probability of interaction between input drug pair.