ArticleBioinformatics (Oxford, England)2021
Using drug descriptions and molecular structures for drug-drug interaction extraction from literature.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 85 citations in OpenAlex.
- Multimodal Collaborative Modeling of Molecular Structures and Biomedical Text for Accurate Drug-Drug Interaction Extraction.Biomedicines · 2026Article
- Knowledge enhancement and full utilization of document information for document-level biomedical relation extraction.Scientific reports · 2025Article
- A meta-contrastive learning approach for clinical drug-drug interaction extraction from biomedical literature.PLoS computational biology · 2025Article
- Pyridazine and pyridazinone compounds in crops protection: a review.Molecular diversity · 2025Review
- Enhanced drug-drug interaction extraction from biomedical text using deep learning-based sentence representations.Scientific reports · 2025Article
- Knowledge-augmented pre-trained language models for biomedical relation extraction.BMC bioinformatics · 2025Article
- Reduction of supervision for biomedical knowledge discovery.BMC bioinformatics · 2025Article
- Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning.Scientific reports · 2025Article
- SCATrans: semantic cross-attention transformer for drug-drug interaction predication through multimodal biomedical data.BMC bioinformatics · 2025Article
- MFE-DDI: A multi-view feature encoding framework for drug-drug interaction prediction.Computational and structural biotechnology journal · 2025Article
- SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.PLoS computational biology · 2024Article
- Deep learning for drug-drug interaction prediction: A comprehensive review.Quantitative biology (Beijing, China) · 2024Review
- MTMG: A multi-task model with multi-granularity information for drug-drug interaction extraction.Heliyon · 2023Article
- Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature.Journal of biomedical semantics · 2023Article
- Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.BMC bioinformatics · 2023Article
- Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature.Bioinformatics (Oxford, England) · 2023Article
- DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction.Frontiers in digital health · 2023Article
- Recent development of machine learning models for the prediction of drug-drug interactions.The Korean journal of chemical engineering · 2023Review
- IMSE: interaction information attention and molecular structure based drug drug interaction extraction.BMC bioinformatics · 2022Article
- A multi-layer soft lattice based model for Chinese clinical named entity recognition.BMC medical informatics and decision making · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationNeural methods to extract drug-drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively utilize external drug database information as well as information from large-scale plain text for DDI extraction. Specifically, we focus on drug description and molecular structure information as the drug database information.
resultsWe evaluated our approach on the DDIExtraction 2013 shared task dataset. We obtained the following results. First, large-scale raw text information can greatly improve the performance of extracting DDIs when combined with the existing model and it shows the state-of-the-art performance. Second, each of drug description and molecular structure information is helpful to further improve the DDI performance for some specific DDI types. Finally, the simultaneous use of the drug description and molecular structure information can significantly improve the performance on all the DDI types. We showed that the plain text, the drug description information and molecular structure information are complementary and their effective combination is essential for the improvement. AVAILABILITY AND IMPLEMENTATION: Our code is available at https://github.com/tticoin/DESC_MOL-DDIE.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.