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Existing machine learning methods for predicting protein\u2013compound interactions are largely divided into those based on molecular structure data and those based on network data. The former utilize information on proteins and compounds, such as amino acid sequences and chemical structures; the latter rely on interaction network data, such as protein\u2013protein interactions and compound\u2013compound interactions. However, there have been few attempts to combine both types of data in molecular information and interaction networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We developed a deep learning-based method that integrates protein features, compound features, and multiple types of interactome data to predict protein\u2013compound interactions. We designed three benchmark datasets with different difficulties and applied them to evaluate the prediction method. The performance evaluations show that our deep learning framework for integrating molecular structure data and interactome data outperforms state-of-the-art machine learning methods for protein\u2013compound interaction prediction tasks. The performance improvement is statistically significant according to the Wilcoxon signed-rank test. This finding reveals that the multi-interactome data captures perspectives other than amino acid sequence homology and chemical structure similarity and that both types of data synergistically improve the prediction accuracy. Furthermore, experiments on the three benchmark datasets show that our method is more robust than existing methods in accurately predicting interactions between proteins and compounds that are unseen in training samples.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13321-021-00513-3","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T08:20:51Z","timestamp":1620030051000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Deep learning integration of molecular and interactome data for protein\u2013compound interaction prediction"],"prefix":"10.1186","volume":"13","author":[{"given":"Narumi","family":"Watanabe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuuto","family":"Ohnuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6932-3452","authenticated-orcid":false,"given":"Yasubumi","family":"Sakakibara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,1]]},"reference":[{"key":"513_CR1","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1093\/bioinformatics\/btm266","volume":"23","author":"N Nagamine","year":"2007","unstructured":"Nagamine N, Sakakibara Y (2007) Statistical prediction of protein\u2013chemical interactions based on chemical structure and mass spectrometry data. 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