Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm.
Comput Biol Med
; 137: 104780, 2021 10.
Article
in English
| MEDLINE | ID: covidwho-1363941
ABSTRACT
BACKGROUND:
Proinflammatory cytokines are correlated with the severity of disease in patients with COVID-19. IL6-mediated activation of STAT3 proliferates proinflammatory responses that lead to cytokine storm promotion. Thus, STAT3 inhibitors may play a crucial role in managing the COVID-19 pathogenesis. The present study discusses a method for predicting inhibitors against the STAT3 signaling pathway.METHOD:
The main dataset comprises 1565 STAT3 inhibitors and 1671 non-inhibitors used for training, testing, and evaluation of models. A number of machine learning classifiers have been implemented to develop the models.RESULTS:
The outcomes of the data analysis show that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. First, we developed models using 2-D and 3-D chemical descriptors and achieved a maximum AUC of 0.84 and 0.73, respectively. Second, fingerprints are used to build predictive models and achieved 0.86 AUC with an accuracy of 78.70% on the validation dataset. Finally, models were developed using hybrid descriptors, which achieved a maximum of 0.87 AUC with 78.55% accuracy on the validation dataset.CONCLUSION:
We used the best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen and Perindopril) to manage the cytokine storm in COVID-19 patients. A webserver "STAT3In" (https//webs.iiitd.edu.in/raghava/stat3in/) has been developed to predict and design STAT3 inhibitors.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Drug Design
/
STAT3 Transcription Factor
/
Cytokine Release Syndrome
/
COVID-19 Drug Treatment
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
Comput Biol Med
Year:
2021
Document Type:
Article
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