{"url":"/dataset/decagon","name":"Decagon","full_name":"Bio-decagon","description_markdown":"Bio-decagon is a dataset for polypharmacy side effect identification problem framed as a multirelational link prediction problem in a two-layer multimodal graph/network of two node types: drugs and proteins. Protein-protein interaction\r\nnetwork describes relationships between proteins. Drug-drug interaction network contains 964 different types of edges (one for each side effect type) and describes which drug pairs lead to which side effects. Lastly,\r\ndrug-protein links describe the proteins targeted by a given drug.\r\n\r\nThe final network after linking entity vocabularies used by different databases has 645 drug and 19,085 protein nodes connected by 715,612 protein-protein, 4,651,131 drug-drug, and 18,596 drug-protein edges.\r\n\r\nSource: [Modeling polypharmacy side effects with graph convolutional networks](https://academic.oup.com/bioinformatics/article/34/13/i457/5045770)\r\nImage Source: [Modeling polypharmacy side effects with graph convolutional networks](http://snap.stanford.edu/decagon/)","description_withheld":null,"homepage":"http://snap.stanford.edu/decagon/","introduced_date":"2018-02-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/modeling-polypharmacy-side-effects-with-graph","title":"Modeling polypharmacy side effects with graph convolutional networks","first_author":"Marinka Zitnik","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Graphs","url":"/datasets/modality/graphs"}],"tasks":[{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"}],"languages":[],"variants":["Decagon"],"data_loaders":[],"num_papers_in_archive":33,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/link-prediction-on-decagon","task":"Link Prediction","dataset_variant":"Decagon","rows":2,"metrics":["AUROC","AUPRC","mAP@50"],"first_row_in_archive_order":{"model":"Decagon","paper":"/paper/modeling-polypharmacy-side-effects-with-graph","metrics":{"AUPRC":"0.832","AUROC":"0.872","mAP@50":"0.803"},"code_links":[{"title":"mims-harvard/decagon","url":"https://github.com/mims-harvard/decagon"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/tri-graph-information-propagation-for","title":"Tri-graph Information Propagation for Polypharmacy Side Effect Prediction","date":"2020-01-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/modeling-polypharmacy-side-effects-with-graph","title":"Modeling polypharmacy side effects with graph convolutional networks","date":"2018-02-02","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}