Lucy Lu Wang, Oyvind Tafjord, Arman Cohan, Sarthak Jain, Sam Skjonsberg, Carissa Schoenick, Nick Botner, Waleed Ammar
| Challenge: | Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. |
| Approach: | They propose an application to search evidence sentences extracted from the literature to identify supplement-drug interactions. |
| Outcome: | The proposed model extracts supplement information and identifies interactions using labeled DDI data. |
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Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information (P18-2)
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| Challenge: | Graph Convolutional Networks (GCNs) can extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Approach: | They propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Outcome: | The proposed model can extract drug-drug interactions (DDIs) from texts with high accuracy and the molecular information can enhance text-based extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set. |
INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Pre-Trained Language Models and Ensemble Learning (2025.naacl-srw)
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| Challenge: | InsightBuddy-AI is a system for extracting medication mentions and their associated attributes. |
| Approach: | They propose a system for extracting medication mentions and their associated attributes . they use stacked and voting ensembles built upon pre-trained language models . |
| Outcome: | The proposed system outperforms fine-tuned models in the extraction of medication mentions and associated attributes. |
RAGPPI: Retrieval-Augmented Generation Benchmark for Protein–Protein Interactions in Drug Discovery (2026.eacl-long)
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| Challenge: | Large Language Models and Retrieval-Augmented Generation (RAG) frameworks have supported Target ID, but no benchmark exists for identifying biological impacts of PPIs. |
| Approach: | They propose to build a factual question-answer benchmark of 4,420 question-announced pairs that focus on the potential biological impacts of PPIs. |
| Outcome: | The proposed benchmark is based on 4,420 question-answer pairs with expert-driven data annotation. |
Amalgamation of protein sequence, structure and textual information for improving protein-protein interaction identification (2020.acl-main)
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| Challenge: | Existing textual methods for protein-protein interaction identification have been used to perform most of the recent PPI tasks in BioNLP domain. |
| Approach: | They propose to incorporate multimodal cues into existing textual data to improve the automatic identification of PPI. |
| Outcome: | The proposed multi-modal datasets outperform baseline methods and unimodal approaches in predicting protein interactions. |
PKAG-DDI: Pairwise Knowledge-Augmented Language Model for Drug-Drug Interaction Event Text Generation (2025.acl-long)
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| Challenge: | Drug-drug interactions arise when multiple drugs are administered concurrently. |
| Approach: | They propose a pairwise knowledge-augmented generative method for DDIE text generation that integrates biological functions from a knowledge set into a language model. |
| Outcome: | The proposed method outperforms existing methods in DDIE text generation on two professional datasets. |
A Dataset for N-ary Relation Extraction of Drug Combinations (2022.naacl-main)
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Aryeh Tiktinsky, Vijay Viswanathan, Danna Niezni, Dana Meron Azagury, Yosi Shamay, Hillel Taub-Tabib, Tom Hope, Yoav Goldberg
| Challenge: | Combination therapies are becoming standard of care for diseases such as cancer, tuberculosis, malaria and HIV. |
| Approach: | They construct an expert-annotated dataset for extracting drug combinations from the scientific literature. |
| Outcome: | The proposed dataset is the first relation extraction dataset consisting of variable-length relations. |
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023.emnlp-main)
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| Challenge: | Existing computational methods for DDI prediction fail to capture interactions for new drugs due to the lack of knowledge. |
| Approach: | They propose a problem setup as zero-shot DDI prediction that deals with the case of new drugs by using textual information from online databases. |
| Outcome: | The proposed method improves on several settings including zero-shot and few-shot DDI prediction and the selected texts are semantically relevant. |
FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction (2025.findings-emnlp)
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| Challenge: | despite advances in DTI models, models often struggle to capture fine-grained interactions between drugs and proteins. |
| Approach: | They propose a novel drug-target interaction model that uses a token-level module to learn fine-grained information for drug-target interactions. |
| Outcome: | The proposed model learns fine-grained information for drug-target interaction . it mitigates sequence fragment invalidation and incorporates the structure-aware vocabulary of target proteins . |
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text (2022.emnlp-main)
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Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He
| Challenge: | Using NLP methods to discover and extract adverse drug events from unstructured textual data is difficult because it requires time-consuming manual curation. |
| Approach: | They propose to use a hierarchical event schema to extract annotated events from medical case reports and biomedical literature to analyze patient data. |
| Outcome: | The proposed dataset is the largest public dataset to date and contains over 5000 events from medical case reports and biomedical literature. |
Extracting Chemical-Protein Interactions via Calibrated Deep Neural Network and Self-training (2020.findings-emnlp)
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| Challenge: | Several natural language processing methods have been used to extract interactions between chemicals and proteins from biomedical text data. |
| Approach: | They propose a method to extract chemical–protein interactions from biomedical text data . they use a pre-trained language-understanding model and calibration techniques to estimate uncertainty . |
| Outcome: | The proposed approach achieves state-of-the-art performance on the Biocreative VI ChemProt task while preserving higher calibration abilities. |