DrugBank is the world’s largest online database of drug and drug-target information. We provide trusted and structured data to researchers who are discovering drugs and innovative approaches.
As we continue to grow our products and knowledge base we have seen ourselves cited in thousands of published papers worldwide. Thanks to our DrugBank Online users, we are helping contribute to groundbreaking discoveries.
Our curation team has pulled together a spotlight of recently published papers on the forefront of innovation and discovery that are using DrugBank to help power their research.
Using DrugBank to predict immunomodulatory target proteins for COVID-19 patients
Researchers were able to develop a computational model to predict immunomodulatory target proteins for COVD-19 patients using a list of selected drugs from DrugBank Online. This prediction process was made possible through the resourcing and integration of DrugBank data.
Their model is able to detect feedback loops and recognize the cell-to-cell communication occurring during an inflammatory response. The research involved potential genes being mapped to DrugBank’s list of drugs to determine inhibitory compounds and their target proteins. Building on this, research found that VCAN and TLR2 were involved in immunomodulatory target responses for COVID-19.
Research of this type can improve early-stage identification of pathological immune responses that occur with persistent inflammatory diseases like COVID-19, and has the potential to provide early responses against viruses that are disrupting our health. As a trusted source for accurate and intelligent data, DrugBank is proud to be able to fuel innovation that is happening faster and more accurately than ever before.
This project involved researchers from:
● Computational Biology Groups of CIC bioGUNE-BRTA (Basque Research and Technology Alliance) at Biskazia Technology Park in Spain
● The Luxembourg Centre for Systems Biomedicine (LCSB) at the University of Luxembourg
Using DrugBank to train DeepCE (AI) to predict gene expression profiles
In February 2021, DrugBank was highlighted in novel research out of the Ohio State University. The research group used the DrugBank database to train their AI program, DeepCE, to predict gene expression profiles.
The team looked at the chemical structures and other drug information on 11,179 drugs in the DrugBank database. This information allows the program to predict gene expression profiles for compounds in the National Institutes of Health (NIH) L1000 dataset that has yet to be experimented on.
The program utilized DrugBank to screen drugs with known gene expression profiles and assign correlation scores with patient gene expression profiles. A more negative score indicated the drugs’ potential to be candidates for the patient of interest.
This program will be helpful in drug repurposing, predicting drug-disease associations, and identifying potential drug candidates for diseases like COVID-19. The program has been used to identify potential candidates to treat COVID-19 as well as 10 candidates with the potential to be repurposed for this disease. Some of these identified drug candidates have been approved for clinical use with different indications (e.g. Cyclosporin and Anidulafungin), while others are investigational. However, many have been evaluated in patients presenting with COVID-19. This could prove valuable in identifying potential therapeutics without running multiple, possibly dead-end, experiments.
This project involved the researchers:
● Ping Zhang (AIMed lab, Ohio State University)
● Thai-Hoang Pham (Ohio State University)
● Yue Qiu (The City University of New York)
● Jucheng Zeng (Ohio State University)
● Lei Xie (The City University of New York)
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Researchers from across the globe, with a range of focuses choose DrugBank as their source for reliable, verified data. From using our structured data to validate machine learning models to finding potential drug candidates, we offer data-driven solutions for many use cases.
DrugBank’s database includes more than 21,000 drug-protein interactions, 130,000 drug product listings, drug-target data that includes proteins, gene identifiers, sequences, and so much more.
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