CORona Drug InTEractions database
In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2
Sebastián A. Cuesta , José R. Mora, Edgar A. Márquez
Abstract
Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha’s test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.
Source: PubMed
Related molecules
| Name | Synonyms | Genes |
|---|---|---|
| Salinomycin sodium | ||
| Digitoxin | ||
| Digoxin | ||
| Niclosamide |
| Target | Target affiliation | Drug | Type | Result |
|---|---|---|---|---|
| Target | Target affiliation | Drug | Type | Result |
| Name | Synonyms | Genes | Origin |
|---|---|---|---|
| Name | Synonyms | Genes | Origin |
| Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
|---|---|---|---|---|---|
| Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
| Title | Authors | DOI | Source | Article type | Date |
|---|---|---|---|---|---|
| Title | Authors | DOI | Source | Article type | Date |
| Title | Status | Phases | Start Date | Prim. Comp. Date | Comp. Date | First Post. Date |
|---|---|---|---|---|---|---|
| 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