{"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/gcn-bmp-investigating-graph-representation","title":"GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task","arxiv_id":null,"date":"2020-07-01","proceeding":"Methods 2020 7","authors":["Xin Chen","Xien Liu","Ji Wu"],"abstract":"One drug's pharmacological activity may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict the occurrence of DDIs. However, existing approaches are almost dependent heavily on various drug-related features, which may incur noisy inductive bias. To alleviate this problem, we investigate the utilization of the end-to-end graph representation learning for the DDI prediction task. We establish a novel DDI prediction method named GCN-BMP (Graph Convolutional Network with Bond-aware Message Propagation) to conduct an accurate prediction for DDIs. Our experiments on two real-world datasets demonstrate that GCN-BMP can achieve higher performance compared to various baseline approaches. Moreover, in the light of the self-contained attention mechanism in our GCN-BMP, we could find the most vital local atoms that conform to domain knowledge with certain interpretability.","url_abs":"https://www.sciencedirect.com/science/article/pii/S1046202320300608","url_pdf":"https://www.sciencedirect.com/sdfe/reader/pii/S1046202320300608/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":"gcn-bmp-investigating-graph-representation","repo_url":"https://github.com/AstraZeneca/chemicalx","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"inductive-bias","task_name":"Inductive Bias"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}