Graph Convolutional Network-Based Screening Strategy for Rapid Identification of SARS-CoV-2 Cell-Entry Inhibitors.
J Chem Inf Model
; 62(8): 1988-1997, 2022 04 25.
Article
in En
| MEDLINE
| ID: mdl-35404596
ABSTRACT
The cell entry of SARS-CoV-2 has emerged as an attractive drug development target. We previously reported that the entry of SARS-CoV-2 depends on the cell surface heparan sulfate proteoglycan (HSPG) and the cortex actin, which can be targeted by therapeutic agents identified by conventional drug repurposing screens. However, this drug identification strategy requires laborious library screening, which is time consuming, and often limited number of compounds can be screened. As an alternative approach, we developed and trained a graph convolutional network (GCN)-based classification model using information extracted from experimentally identified HSPG and actin inhibitors. This method allowed us to virtually screen 170,000 compounds, resulting in â¼2000 potential hits. A hit confirmation assay with the uptake of a fluorescently labeled HSPG cargo further shortlisted 256 active compounds. Among them, 16 compounds had modest to strong inhibitory activities against the entry of SARS-CoV-2 pseudotyped particles into Vero E6 cells. These results establish a GCN-based virtual screen workflow for rapid identification of new small molecule inhibitors against validated drug targets.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Antiviral Agents
/
Virus Internalization
/
SARS-CoV-2
Type of study:
Diagnostic_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
J Chem Inf Model
Journal subject:
INFORMATICA MEDICA
/
QUIMICA
Year:
2022
Document type:
Article
Affiliation country:
United States