Drug–drug Interaction Extraction
13 papers with code • 3 benchmarks • 3 datasets
Automatic extraction of Drug-drug interaction (DDI) information from the biomedical literature.
( Image credit: Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature )
Benchmarks
These leaderboards are used to track progress in Drug–drug Interaction Extraction
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Libraries
Use these libraries to find Drug–drug Interaction Extraction models and implementations
DDI
Drug Combination Extraction Dataset
DrugBank
Most implemented papers
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
dmis-lab/biobert
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Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
bionlu-coling2024/biomed-ner-intent_detection
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In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters
helboukkouri/character-bert
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COLING 2020
Due to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not being intrinsically linked to the notion of Transformers.
ELECTRAMed: a new pre-trained language representation model for biomedical NLP
The overwhelming amount of biomedical scientific texts calls for the development of effective language models able to tackle a wide range of biomedical natural language processing (NLP) tasks.
SSI–DDI: Substructure–Substructure Interactions for Drug–Drug Interaction Prediction
kanz76/ssi-ddi
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Briefings in Bioinformatics 2021
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug–drug interactions (DDIs), which can cause serious injuries to the organism.
A Dataset for N-ary Relation Extraction of Drug Combinations
allenai/drug-combo-extraction
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NAACL 2022
Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task.
Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
sunilitggu/DDI-extraction-through-LSTM
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The two models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the output of Bi-LSTM layer to assign weights to features.
Drug-Drug Adverse Effect Prediction with Graph Co-Attention
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects.
Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature
Specifically, we focus on drug description and molecular structure information as the drug database information.
