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However, searching for effective drug combinations remains a major challenge due to the vast number of possible combinations. Biomedical knowledge graph (KG)-based methods have shown potential in predicting effective combinations for wide spectrum of diseases, but the lack of credible negative samples has limited the prediction performance of machine learning models. To address this issue, we propose a novel model-agnostic framework that leverages existing drug\u2013drug interaction (DDI) data as a reliable negative dataset and employs supervised contrastive learning (SCL) to transform drug embedding vectors to be more suitable for drug combination prediction. We conducted extensive experiments using various network embedding algorithms, including random walk and graph neural networks, on a biomedical KG. Our framework significantly improved performance metrics compared to the baseline framework. We also provide embedding space visualizations and case studies that demonstrate the effectiveness of our approach. This work highlights the potential of using DDI data and SCL in finding tighter decision boundaries for predicting effective drug combinations.<\/jats:p>","DOI":"10.1093\/bib\/bbad285","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:26:21Z","timestamp":1691367981000},"source":"Crossref","is-referenced-by-count":13,"title":["A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug\u2013drug interaction data and supervised contrastive learning"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7136-204X","authenticated-orcid":false,"given":"Jeonghyeon","family":"Gu","sequence":"first","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University , 1, Gwanak-ro, 08826 Seoul , Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9217-8380","authenticated-orcid":false,"given":"Dongmin","family":"Bang","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioinformatics, Seoul National University , 1, Gwanak-ro, 08826 Seoul , Republic of Korea"},{"name":"AIGENDRUG Co., Ltd. , 1, Gwanak-ro, 08826 Seoul , Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5127-7224","authenticated-orcid":false,"given":"Jungseob","family":"Yi","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Artificial Intelligence, Seoul National University , 1, Gwanak-ro, 08826 Seoul , Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7398-9580","authenticated-orcid":false,"given":"Sangseon","family":"Lee","sequence":"additional","affiliation":[{"name":"Institute of Computer Technology Seoul National University , 1, Gwanak-ro, 08826 Seoul , Republic of 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