{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/drug-drug-adverse-effect-prediction-with","title":"Drug-Drug Adverse Effect Prediction with Graph Co-Attention","arxiv_id":"1905.00534","date":"2019-05-02","proceeding":null,"authors":["Andreea Deac","Yu-Hsiang Huang","Petar Veličković","Pietro Liò","Jian Tang"],"abstract":"Complex or co-existing diseases are commonly treated using drug combinations,\nwhich can lead to higher risk of adverse side effects. The detection of\npolypharmacy side effects is usually done in Phase IV clinical trials, but\nthere are still plenty which remain undiscovered when the drugs are put on the\nmarket. Such accidents have been affecting an increasing proportion of the\npopulation (15% in the US now) and it is thus of high interest to be able to\npredict the potential side effects as early as possible. Systematic\ncombinatorial screening of possible drug-drug interactions (DDI) is challenging\nand expensive. However, the recent significant increases in data availability\nfrom pharmaceutical research and development efforts offer a novel paradigm for\nrecovering relevant insights for DDI prediction. Accordingly, several recent\napproaches focus on curating massive DDI datasets (with millions of examples)\nand training machine learning models on them. Here we propose a neural network\narchitecture able to set state-of-the-art results on this task---using the type\nof the side-effect and the molecular structure of the drugs alone---by\nleveraging a co-attentional mechanism. In particular, we show the importance of\nintegrating joint information from the drug pairs early on when learning each\ndrug's representation.","url_abs":"http://arxiv.org/abs/1905.00534v1","url_pdf":"http://arxiv.org/pdf/1905.00534v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"drug-drug-adverse-effect-prediction-with","repo_url":"https://github.com/AstraZeneca/chemicalx","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"drug-drug-interaction-extraction","task_name":"Drug–drug Interaction Extraction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drug-drug-interaction-extraction-on-drugbank","task":"Drug–drug Interaction Extraction","dataset":"DrugBank","model":"MHCA-DDI","rank_in_archive_order":3,"of":3,"metrics":{"AUROC":"86.33","Accuracy":"78.51","F1 score":"83.31"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1905.00534","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}