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We have evaluated nine knowledge graph embedding models from a selection of geometric, decomposition, and convolutional models on this prediction task. We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. Furthermore, we have implemented a fine-tuning architecture which adapts the knowledge graph embeddings to the effect prediction task and leads to a better performance. Finally, we evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.<\/jats:p>","DOI":"10.3233\/sw-222804","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T14:30:48Z","timestamp":1648564248000},"page":"299-338","source":"Crossref","is-referenced-by-count":8,"title":["Prediction of adverse biological effects of chemicals using knowledge graph embeddings"],"prefix":"10.1177","volume":"13","author":[{"given":"Erik B.","family":"Myklebust","sequence":"first","affiliation":[{"name":"Norwegian Institute for Water Research, Oslo, Norway"},{"name":"SIRIUS, University of Oslo, Oslo, Norway"},{"name":"NORSAR, Kjeller, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ernesto","family":"Jim\u00e9nez-Ruiz","sequence":"additional","affiliation":[{"name":"SIRIUS, University of Oslo, Oslo, Norway"},{"name":"City, University of London, London, United 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