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SUPP.AI: detecting supplement-drug interactions

This repository contains data used to detect dietary supplement interactions from scientific articles on SUPP.AI. We train the RoBERTa-DDI model using drug-drug interaction data from the DDI-2013 and NLM-DailyMed datasets. We use this model to extract evidence sentences for supplement interactions from 22M articles in Semantic Scholar.

The resulting interactions are available for search at SUPP.AI.

Extracted evidence is available for bulk download here.

See our arXiv preprint for implementation and data details.

Please address feedback to lucyw [at] allenai [dot] org.


RoBERTa-DDI uses pre-trained representations from the RoBERTa language model and fine-tunes these representations on DDI classification data. The model is implemented using AllenNLP.

Training data

Training data is derived from the DDI-2013 and NLM-DailyMed datasets. Train/test splits are preserved from the Merged PDDI data release. Development sets are split from the training set for each of the two datasets. Additional pre-processing is performed to create pairwise combinations of entities from each sentence.

Train/Dev/Test splits are available in training_data/.

Supplement interaction evaluation data

A set of 500 sentences are manually labeled for the presence or absence of a supplement-related interaction. These labels are provided in sdi_eval.tsv.

Performance of RoBERTa-DDI on the DDI and SDI test sets is given below:

Test set Precision Recall F1-score
Drugs (DDI-2013) 0.90 0.87 0.88
Drugs (NLM-DailyMed) 0.83 0.85 0.84
SDI-eval 0.82 0.58 0.68

UMLS CUI clusters

We leverage UMLS Metathesaurus identifiers (CUIs) to identify supplement and drug entities. We perform filtering and clustering to create a list of supplement and drug identifiers which we surface on SUPP.AI. These identifier clusters are available at cui_clusters.json.


If using this data, please cite our arXiv preprint:

  title={Extracting evidence of supplement-drug interactions from literature},
  author={Lucy Lu Wang and Oyvind Tafjord and Sarthak Jain and Arman Cohan and Sam Skjonsberg and Carissa Schoenick and Nick Botner and Waleed Ammar},


Data and models for SDI detection (SUPP.AI)



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