{"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/r-bert-cnn-drug-target-interactions","title":"R-BERT-CNN: Drug-target interactions extraction from biomedical literature","arxiv_id":"2111.00611","date":"2021-10-31","proceeding":null,"authors":["Jehad Aldahdooh","Ziaurrehman Tanoli","Jing Tang"],"abstract":"In this research, we present our work participation for the DrugProt task of BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing, which are often manually extracted from the experimental articles. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowledge base is challenging. To solve this issue, we provide a solution for Track 1, which aims to extract 10 types of interactions between drug and protein entities. We applied an Ensemble Classifier model that combines BioMed-RoBERTa, a state of art language model, with Convolutional Neural Networks (CNN) to extract these relations. Despite the class imbalances in the BioCreative VII DrugProt test corpus, our model achieves a good performance compared to the average of other submissions in the challenge, with the micro F1 score of 55.67% (and 63% on BioCreative VI ChemProt test corpus). The results show the potential of deep learning in extracting various types of DTIs.","url_abs":"https://arxiv.org/abs/2111.00611v1","url_pdf":"https://arxiv.org/pdf/2111.00611v1.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":[],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"drugprot","task_name":"DrugProt"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drugprot-on-drugprot-1","task":"DrugProt","dataset":"DrugProt","model":"R-BERT-CNN","rank_in_archive_order":1,"of":1,"metrics":{"F1 (micro)":"55.67"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}