# Vapur: A Search Engine to Find Related Protein-Compound Pairs in COVID-19 Literature

arXiv: https://arxiv.org/abs/2009.02526 · HTML: https://mlsanity.com/paper/2009.02526/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [BERN2: an advanced neural biomedical named entity recognition and normalization tool](https://mlsanity.com/paper/2201.02080/) (2022)
- [Augmenting Biomedical Named Entity Recognition with General-domain Resources](https://mlsanity.com/paper/2406.10671/) (2024)
- [Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction](https://mlsanity.com/paper/2106.08252/) (2021)

## Abstract (arXiv)

Coronavirus Disease of 2019 (COVID-19) created dire consequences globally and triggered an intense scientific effort from different domains. The resulting publications created a huge text collection in which finding the studies related to a biomolecule of interest is challenging for general purpose search engines because the publications are rich in domain specific terminology. Here, we present Vapur: an online COVID-19 search engine specifically designed to find related protein - chemical pairs. Vapur is empowered with a relation-oriented inverted index that is able to retrieve and group studies for a query biomolecule with respect to its related entities. The inverted index of Vapur is automatically created with a BioNLP pipeline and integrated with an online user interface. The online interface is designed for the smooth traversal of the current literature by domain researchers and is publicly available at https://tabilab.cmpe.boun.edu.tr/vapur/ .