CORona Drug InTEractions database
PolypharmDB, a Deep Learning-Based Resource, Quickly Identifies Repurposed Drug Candidates for COVID-19
Dar'ya S. Redka Stephen S. MacKinnon Melissa Landon Andreas Windemuth Naheed Kurji Vijay Shahani
Abstract
There is an immediate need to discover treatments for COVID-19, the pandemic caused by the SARS-CoV-2 virus. Standard small molecule drug discovery workflows that start with library screens are an impractical path forward given the timelines to discover, develop, and test clinically. To accelerate the time to patient testing, here we explored the therapeutic potential of small molecule drugs that have been tested to some degree in a clinical environment, including approved medications, as possible therapeutic interventions for COVID-19. Motivating our process is a concept termed polypharmacology, i.e. off-target interactions that may hold therapeutic potential. In this work, we used Ligand Design, our deep learning drug design platform, to interrogate the polypharmacological profiles of an internal collection of small molecule drugs with federal approval or going through clinical trials, with the goal of identifying molecules predicted to modulate targets relevant for COVID-19 treatment. Resulting from our efforts is PolypharmDB, a resource of drugs and their predicted binding of protein targets across the human proteome. Mining PolypharmDB yielded molecules predicted to interact with human and viral drug targets for COVID-19, including host proteins linked to viral entry and proliferation and key viral proteins associated with the virus life-cycle. Further, we assembled a collection of prioritized approved drugs for two specific host-targets, TMPRSS2 and cathepsin B, whose joint inhibition was recently shown to block SARS-CoV-2 virus entry into host cells. Overall, we demonstrate that our approach facilitates rapid response, identifying 30 prioritized candidates for testing for their possible use as anti-COVID drugs. Using the PolypharmDB resource, it is possible to identify repurposed drug candidates for newly discovered targets within a single work day. We are making a complete list of the molecules we identified available at no cost to partners with the ability to test them for efficacy, in vitro and/or clinically.
Source: ChemRxiv
Related molecules
Related interactions
Target | Target affiliation | Drug | Type | Result |
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Target | Target affiliation | Drug | Type | Result |
Name | Synonyms | Genes | Origin |
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Name | Synonyms | Genes | Origin |
Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
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Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
Title | Authors | DOI | Source | Article type | Date |
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Title | Authors | DOI | Source | Article type | Date |
Title | Status | Phases | Start Date | Prim. Comp. Date | Comp. Date | First Post. Date |
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Title | Status | Phases | Start Date | Prim. Comp. Date | Comp. Date | First Post. Date |
CORDITE (CORona Drug InTEractions database) collects and aggregates data from PubMed, MedRxiv, BioRxiv, ChemRxiv and PMC for SARS-CoV-2. Its main focus is set on drug interactions either addressing viral proteins or human proteins that could be used to treat COVID. It collects and provides up-to-date information on computational predictions, in vitro, as well as in vivo study data.
The information provided is for research only and we cannot guarantee the correctness of the data.
Please contact dominik.heider@uni-muenster.de for further information.
Programmable access
There is an open API for access programmatically to the database. The API will print a JSON output:
- Interactions
https://cordite-api.uni-muenster.de/api.php?action=list&table=interaction
- Targets
https://cordite-api.uni-muenster.de/api.php?action=list&table=target
- Drugs
https://cordite-api.uni-muenster.de/api.php?action=list&table=drug
- Publications
https://cordite-api.uni-muenster.de/api.php?action=list&table=publication
- Clinical trials
https://cordite-api.uni-muenster.de/api.php?action=list&table=clinical_trial