Anti-SARS-CoV-2 Ability Prediction
We construct machine learning models to quickly identify potential compounds actively targeting to COVID-19 virus.
Classification Result
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Status: Failed
Status: Success
| Type | Assay | Result | Probability | Prediction |
|---|---|---|---|---|
| Viral Replication | 3CL enzymatic activity | |||
| Viral Entry | ACE2 enzymatic activity | |||
| TMPRSS2 enzymatic activity | ||||
| Spike-ACE2 protein-protein interaction AlphaLISA | ||||
| Spike-ACE2 protein-protein interaction TruHit Counterscreen | ||||
| Live Virus Infectivity | HEK293 cell line toxicity | |||
| Human fibroblast toxicity | ||||
| SARS-CoV-2 cytopathic effect CPE | ||||
| SARS-CoV-2 cytopathic effect host tox counterscreen |
Promising drug meet these criteria:
1. is active 3CL Protease inhibitor 2. is active ACE2 inhibitor 3. is not cytotoxic (HEK293 cell line and human fibroblast) 4. is active in CPE 5. is active in Spike/ACE2 6. is not active in the counterscreen 7. is active TMPRSS2 protease inhibitor
Predicition is implemented by machine learning modeling using k-nearest neightbors (KNN) algorithm.
Prediction File
Descriptors
Similarity Search
Set the similarity threshold and see how many compounds could be found from our drug dataset.
Compounds that are >
similar to input molecules will be selected from ZINC database.